Genetic polymorphisms associated with psoriasis, methods of detection and uses thereof

ABSTRACT

The present invention is based on the discovery of genetic polymorphisms that are associated with psoriasis and related pathologies. In particular, the present invention relates to nucleic acid molecules containing the polymorphisms, including groups of nucleic acid molecules that may be used as a signature marker set, such as a haplotype, a diplotype, variant proteins encoded by such nucleic acid molecules, reagents for detecting the polymorphic nucleic acid molecules and proteins, and methods of using the nucleic acid and proteins as well as methods of using reagents for their detection.

FIELD OF THE INVENTION

The present invention is in the field of diagnosis and therapy of psoriasis. In particular, the present invention relates to specific single nucleotide polymorphisms (SNPS) in the human genome, and their association with psoriasis and related pathologies. Based on differences in allele frequencies in the psoriasis patient population relative to normal individuals, the naturally-occurring SNPs disclosed herein can be used as targets for the design of diagnostic reagents and the development of therapeutic agents, as well as for disease association and linkage analysis. In particular, the SNPs of the present invention are useful for identifying an individual who is at an increased or decreased risk of developing psoriasis and for early detection of the disease, for providing clinically important information for the prevention and/or treatment of psoriasis, and for screening and selecting therapeutic agents. The SNPs disclosed herein are also useful for human identification applications. Methods, assays, kits, and reagents for detecting the presence of these polymorphisms and their encoded products are provided.

BACKGROUND OF THE INVENTION

Psoriasis is a common, chronic, T-cell-mediated inflammatory disease of the skin affecting ˜2-3% of whites of European descent. Although this disease is found in all populations, its prevalence is lower in Asians and African-Americans and also declines at lower latitudes. The most common form, psoriasis vulgaris, is characterized by varying numbers of red, raised, scaly skin patches that can be present on any body surface, but most often appear on the elbows, knees and scalp. The onset of disease usually occurs early in life (15-30 years) and affects males and females equally. Up to 30% of individuals with psoriasis will develop an inflammatory arthritis, which can affect the peripheral joints of the hands and feet, large joints, or the central axial-skeleton. Pathologically, psoriasis is characterized by vascular changes, hyperproliferation of keratinocytes, altered epidermal differentiation and inflammation. In particular, the reaction of cells in the epidermis to type 1 effector molecules produced by T-cells results in the characteristic pathology of the plaques.

The genetics of psoriasis are complex and highly heritable as evidenced by an increased rate of concordance in monozygotic twins over dizygotic twins (35%-72% vs. 12-23%) and a substantially increased incidence in family members of affected individuals (first-degree relatives 6%); however, it is clear that environmental effects are also responsible for disease susceptibility. Ten genome-wide linkage scans have resulted in strong evidence for a susceptibility locus in the MHC region on 6p21 (PSORS1 [MIM 177900]), but have not yielded consistent evidence for other regions.

Linkage and association in the MHC (6p21) are thought to be due to HLA-C, in particular psoriasis susceptibility effects are thought to be caused by the *0602 allele, although other candidate genes in the area may also contribute to disease predisposition. Association studies have identified three genes under linkage peaks, with considerable evidence for linkage disequilibrium with psoriasis, namely SLC9A3R1/NAT9 and RAPTOR (KIAA1303) in 17q25, and SLC12A8 in 3q21. Several other genes including VDR, MMP2, IL10, IL1RN, IL12B, and IRF2 (Genetic Association Database, OMIM) have been associated with psoriasis in sample sets of varying sizes and of different ethnicities; however, without more data from additional independent studies, it is difficult to draw statistically sound conclusions about whether these markers are truly associated with disease. Thus, there remains a need for the discovery of reliable markers that can associate themselves with psoriasis, and in turn, would facilitate the diagnosis and treatment of the disease.

The discovery of genetic markers which are useful in identifying psoriasis individuals who are at increased risk for developing psoriasis may lead to, for example, better therapeutic strategies, economic models, and health care policy decisions.

SNPs

The genomes of all organisms undergo spontaneous mutation in the course of their continuing evolution, generating variant forms of progenitor genetic sequences (Gusella, Ann. Rev. Biochem. 55, 831-854 (1986)). A variant form may confer an evolutionary advantage or disadvantage relative to a progenitor form or may be neutral. In some instances, a variant form confers an evolutionary advantage to the species and is eventually incorporated into the DNA of many or most members of the species and effectively becomes the progenitor form. Additionally, the effects of a variant form may be both beneficial and detrimental, depending on the circumstances. For example, a heterozygous sickle cell mutation confers resistance to malaria, but a homozygous sickle cell mutation is usually lethal. In many cases, both progenitor and variant forms survive and co-exist in a species population. The coexistence of multiple forms of a genetic sequence gives rise to genetic polymorphisms, including SNPs.

Approximately 90% of all polymorphisms in the human genome are SNPs. SNPs are single base positions in DNA at which different alleles, or alternative nucleotides, exist in a population. The SNP position (interchangeably referred to herein as SNP, SNP site, SNP locus, SNP marker, or marker) is usually preceded by and followed by highly conserved sequences of the allele (e.g., sequences that vary in less than 1/100 or 1/1000 members of the populations). An individual may be homozygous or heterozygous for an allele at each SNP position. A SNP can, in some instances, be referred to as a “cSNP” to denote that the nucleotide sequence containing the SNP is an amino acid coding sequence.

A SNP may arise from a substitution of one nucleotide for another at the polymorphic site. Substitutions can be transitions or transversions. A transition is the replacement of one purine nucleotide by another purine nucleotide, or one pyrimidine by another pyrimidine. A transversion is the replacement of a purine by a pyrimidine, or vice versa. A SNP may also be a single base insertion or deletion variant referred to as an “indel” (Weber et al., “Human diallelic insertion/deletion polymorphisms”, Am J Hum Genet. 2002 October; 71(4):855-82).

A synonymous codon change, or silent mutation/SNP (terms such as “SNP”, “polymorphism”, “mutation”, “mutant”, “variation”, and “variant” are used herein interchangeably), is one that does not result in a change of amino acid due to the degeneracy of the genetic code. A substitution that changes a codon coding for one amino acid to a codon coding for a different amino acid (i.e., a non-synonymous codon change) is referred to as a missense mutation. A nonsense mutation results in a type of non-synonymous codon change in which a stop codon is formed, thereby leading to premature termination of a polypeptide chain and a truncated protein. A read-through mutation is another type of non-synonymous codon change that causes the destruction of a stop codon, thereby resulting in an extended polypeptide product. While SNPs can be bi-, tri-, or tetra-allelic, the vast majority of the SNPs are bi-allelic, and are thus often referred to as “bi-allelic markers”, or “di-allelic markers”.

As used herein, references to SNPs and SNP genotypes include individual SNPs and/or haplotypes, which are groups of SNPs that are generally inherited together. Haplotypes can have stronger correlations with diseases or other phenotypic effects compared with individual SNPs, and therefore may provide increased diagnostic accuracy in some cases (Stephens et al. Science 293, 489-493, 20 Jul. 2001). As used herein, the term “haplotype” refers to a set of two or more alleles on a single chromosome. The term “diplotype” refers to a combination of two haplotypes that a diploid individual carries. The term “double diplotype”, also called “two-locus diplotype”, refers to a combination of diplotypes at two distinct loci for an individual.

Causative SNPs are those SNPs that produce alterations in gene expression or in the expression, structure, and/or function of a gene product, and therefore are most predictive of a possible clinical phenotype. One such class includes SNPs falling within regions of genes encoding a polypeptide product, i.e. cSNPs. These SNPs may result in an alteration of the amino acid sequence of the polypeptide product (i.e., non-synonymous codon changes) and give rise to the expression of a defective or other variant protein. Furthermore, in the case of nonsense mutations, a SNP may lead to premature termination of a polypeptide product. Such variant products can result in a pathological condition, e.g., genetic disease. Examples of genes in which a SNP within a coding sequence causes a genetic disease include sickle cell anemia and cystic fibrosis.

Causative SNPs do not necessarily have to occur in coding regions; causative SNPs can occur in, for example, any genetic region that can ultimately affect the expression, structure, and/or activity of the protein encoded by a nucleic acid. Such genetic regions include, for example, those involved in transcription, such as SNPs in transcription factor binding domains, SNPs in promoter regions, in areas involved in transcript processing, such as SNPs at intron-exon boundaries that may cause defective splicing, or SNPs in mRNA processing signal sequences such as polyadenylation signal regions. Some SNPs that are not causative SNPs nevertheless are in close association with, and therefore segregate with, a disease-causing sequence. In this situation, the presence of a SNP correlates with the presence of, or predisposition to, or an increased risk in developing the disease. These SNPs, although not causative, are nonetheless also useful for diagnostics, disease predisposition screening, and other uses.

An association study of a SNP and a specific disorder involves determining the presence or frequency of the SNP allele in biological samples from individuals with the disorder of interest, such as psoriasis and related pathologies and comparing the information to that of controls (i.e., individuals who do not have the disorder; controls may be also referred to as “healthy” or “normal” individuals) who are preferably of similar age and race. The appropriate selection of patients and controls is important to the success of SNP association studies. Therefore, a pool of individuals with well-characterized phenotypes is extremely desirable.

A SNP may be screened in diseased tissue samples or any biological sample obtained from a diseased individual, and compared to control samples, and selected for its increased (or decreased) occurrence in a specific pathological condition, such as pathologies related to psoriasis, increased or decreased risk of developing psoriasis. Once a statistically significant association is established between one or more SNP(s) and a pathological condition (or other phenotype) of interest, then the region around the SNP can optionally be thoroughly screened to identify the causative genetic locus/sequence(s) (e.g., causative SNP/mutation, gene, regulatory region, etc.) that influences the pathological condition or phenotype. Association studies may be conducted within the general population and are not limited to studies performed on related individuals in affected families (linkage studies).

Clinical trials have shown that patient response to treatment with pharmaceuticals is often heterogeneous. There is a continuing need to improve pharmaceutical agent design and therapy. In that regard, SNPs can be used to identify patients most suited to therapy with particular pharmaceutical agents (this is often termed “pharmacogenomics”). Similarly, SNPs can be used to exclude patients from certain treatment due to the patient's increased likelihood of developing toxic side effects or their likelihood of not responding to the treatment. Pharmacogenomics can also be used in pharmaceutical research to assist the drug development and selection process. (Linder et al. (1997), Clinical Chemistry, 43, 254; Marshall (1997), Nature Biotechnology, 15, 1249; International Patent Application WO 97/40462, Spectra Biomedical; and Schafer et al. (1998), Nature Biotechnology, 16: 3).

SUMMARY OF THE INVENTION

The present invention relates to the identification of novel SNPs, unique combinations of such SNPs, haplotypes or diplotypes of SNPs that are associated with psoriasis and in particular the increased or decreased risk of developing psoriasis. The polymorphisms disclosed herein are directly useful as targets for the design of diagnostic reagents and the development of therapeutic agents for use in the diagnosis and treatment of psoriasis and related pathologies.

Based on the identification of SNPs associated with psoriasis, the present invention also provides methods of detecting these variants as well as the design and preparation of detection reagents needed to accomplish this task. The invention specifically provides, for example, novel SNPs in genetic sequences involved in psoriasis and related pathologies; isolated nucleic acid molecules (including, for example, DNA and RNA molecules) containing these SNPs, variant proteins encoded by nucleic acid molecules containing such SNPs, antibodies to the encoded variant proteins, computer-based and data storage systems containing the novel SNP information, methods of detecting these SNPs in a test sample, methods of identifying individuals who have an altered (i.e., increased or decreased) risk of developing psoriasis based on the presence or absence of one or more particular nucleotides (alleles) at one or more SNP sites disclosed herein or the detection of one or more encoded variant products (e.g., variant mRNA transcripts or variant proteins), methods of identifying individuals who are more or less likely to respond to a treatment (or more or less likely to experience undesirable side effects from a treatment, etc.), methods of screening for compounds useful in the treatment of a disorder associated with a variant gene/protein, compounds identified by these methods, methods of treating disorders mediated by a variant gene/protein, methods of using the novel SNPs of the present invention for human identification, etc.

In Tables 1-2, the present invention provides gene information, transcript sequences (SEQ ID NOS: 1-2), encoded amino acid sequences (SEQ ID NOS:3-4), genomic sequences (SEQ ID NOS:10-18), transcript-based context sequences (SEQ ID NOS:5-9) and genomic-based context sequences (SEQ ID NOS:19-110) that contain the SNPs of the present invention, and extensive SNP information that includes observed alleles, allele frequencies, populations/ethnic groups in which alleles have been observed, information about the type of SNP and corresponding functional effect, and, for cSNPs, information about the encoded polypeptide product. The transcript sequences (SEQ ID NOS:1-2), amino acid sequences (SEQ ID NOS:3-4), genomic sequences (SEQ ID NOS: 10-18), transcript-based SNP context sequences (SEQ ID NOS: 5-9), and genomic-based SNP context sequences (SEQ ID NOS:19-110) are also provided in the Sequence Listing.

In a specific embodiment of the present invention, SNPs that occur naturally in the human genome are provided as isolated nucleic acid molecules. These SNPs are associated with psoriasis and related pathologies. In particular the SNPs are associated with either an increased or decreased risk of developing psoriasis. As such, they can have a variety of uses in the diagnosis and/or treatment of psoriasis and related pathologies. One aspect of the present invention relates to an isolated nucleic acid molecule comprising a nucleotide sequence in which at least one nucleotide is a SNP that is propriatory to Applera, or Celera. In an alternative embodiment, a nucleic acid of the invention is an amplified polynucleotide, which is produced by amplification of a SNP-containing nucleic acid template. In another embodiment, the invention provides for a variant protein that is encoded by a nucleic acid molecule containing a SNP disclosed herein.

In yet another embodiment of the invention, a reagent for detecting a SNP in the context of its naturally-occurring flanking nucleotide sequences (which can be, e.g., either DNA or mRNA) is provided. In particular, such a reagent may be in the form of, for example, a hybridization probe or an amplification primer that is useful in the specific detection of a SNP of interest. In an alternative embodiment, a protein detection reagent is used to detect a variant protein that is encoded by a nucleic acid molecule containing a SNP disclosed herein. A preferred embodiment of a protein detection reagent is an antibody or an antigen-reactive antibody fragment.

In another embodiment of the invention, applicants teach a method of determining whether a human has an altered risk (either increased risk or decreased risk) of developing psoriasis or other related diseases such as Crohn's Diseases, comprising testing nucleic acid from said human for the presence of absence of a polymorphism selected from the exon 4 LD block of IL13, wherein the presence of said polymorphism indicates that said human has an altered risk of developing psoriasis or other related diseases such as Crohn's Diseases. Examples of such polymorphisms are disclosed in Table 1 and Table 2.

In a further embodiment of the invention, applicants teach a method of further correlating the presence or absence of the SNP with the altered risk for psoriasis or Crohn's Diseases for said human.

Various embodiments of the invention also provide kits comprising SNP detection reagents, and methods for detecting the SNPs disclosed herein by employing detection reagents. In a specific embodiment, the present invention provides for a method of identifying an individual having an increased or decreased risk of developing psoriasis by detecting the presence or absence of one or more SNP alleles disclosed herein. Preferably, the SNP allele can be an allele of a SNP selected from the group consisting of rs1800925, rs20541, and rs848, or a combination of any number of them.

In another embodiment, a method for diagnosis of psoriasis and related pathologies by detecting the presence or absence of one or more SNP alleles disclosed herein is provided. In another embodiment, the invention provides for a method of identifying an individual having an altered (either increased, or, decreased) risk of developing psoriasis by detecting the presence or absence of one or more SNP haplotypes disclosed herein.

The SNP haplotype herein can be, for example, a combination of rs1800925(C), rs20541(C), and rs848(G), as a risk haplotype (interchangeably referred to as a susceptible haplotype). The SNP haplotype herein can also be, for example, a combination of rs1800925(T), rs20541(T), and rs848(T), as a protective haplotype.

Furthermore, an exemplary embodiment of the invention provides for a method of identifying an individual having an increased risk of developing psoriasis by detecting the presence or absence of one or more SNP haplotypes. Preferably, the SNP haplotype can be a combination of rs1800925(C), rs20541 (C), and rs848(G). An alternative exemplary embodiment of the invention provides for a method of identifying an individual having a decreased risk of developing psoriasis by detecting the presence or absence of one or more SNP haplotypes. Preferably, the SNP haplotype can be a combination of rs1800925(T), rs20541(T), and rs848(T).

The nucleic acid molecules of the invention can be inserted in an expression vector, such as to produce a variant protein in a host cell. Thus, the present invention also provides for a vector comprising a SNP-containing nucleic acid molecule, genetically-engineered host cells containing the vector, and methods for expressing a recombinant variant protein using such host cells. In another specific embodiment, the host cells, SNP-containing nucleic acid molecules, and/or variant proteins can be used as targets in a method for screening and identifying therapeutic agents or pharmaceutical compounds useful in the treatment of psoriasis and related pathologies.

An aspect of this invention is a method for treating psoriasis in a human subject wherein said human subject harbors a SNP, gene, transcript, and/or encoded protein identified in Tables 1-2, which method comprises administering to said human subject a therapeutically or prophylactically effective amount of one or more agents counteracting the effects of the disease, such as by inhibiting (or stimulating) the activity of the gene, transcript, and/or encoded protein identified in Tables 1-2.

Another aspect of this invention is a method for identifying an agent useful in therapeutically or prophylactically treating psoriasis and related pathologies in a human subject wherein said human subject harbors a SNP, gene, transcript, and/or encoded protein identified in Tables 1-2, which method comprises contacting the gene, transcript, or encoded protein with a candidate agent under conditions suitable to allow formation of a binding complex between the gene, transcript, or encoded protein and the candidate agent and detecting the formation of the binding complex, wherein the presence of the complex identifies said agent.

Another aspect of this invention is a method for treating psoriasis and related pathologies in a human subject, which method comprises:

(i) determining that said human subject harbors a SNP, gene, transcript, and/or encoded protein identified in Tables 1-2, and

(ii) administering to said subject a therapeutically or prophylactically effective amount of one or more agents counteracting the effects of the disease.

Yet another aspect of this invention is a method for evaluating the suitability of a patient for psoriasis treatment comprising determining the genotype of said patient with respect to a particular set of SNP markers, said SNP markers comprising a plurality of individual SNPs ranging from two to seven SNPs in Table 1 or Table 2, and calculating a score using an appropriate algorithm based on the genotype of said patient, the resulting score being indicative of the suitability of said patient undergoing psoriasis treatment.

Another aspect of the invention is a method of treating a psoriasis patient comprising administering an appropriate drug in a therapeutically effective amount to said psoriasis patient whose genotype has been shown to contain a plurality of SNPs as described in Table 1 or Table 2.

Many other uses and advantages of the present invention will be apparent to those skilled in the art upon review of the detailed description of the preferred embodiments herein. Solely for clarity of discussion, the invention is described in the sections below by way of non-limiting examples.

DESCRIPTION OF THE SEQUENCE LISTING

File CD000018ORD_SEQLIST.TXT provides the Sequence Listing in text (ASCII) format. The Sequence Listing provides the transcript sequences (SEQ ID NOS:1-2) and protein sequences (SEQ ID NOS:3-4) as shown in Table 1, and genomic sequence (SEQ ID NO:10-18) as shown in Table 2, for each liver fibrosis-associated gene that contains one or more SNPs of the present invention. Also provided in the Sequence Listing are context sequences flanking each SNP, including both transcript-based context sequences as shown in Table 1 (SEQ ID NOS:5-9) and genomic-based context sequences as shown in Table 2 (SEQ ID NOS:19-110). The context sequences generally provide 100 bp upstream (5′) and 100 bp downstream (3′) of each SNP, with the SNP in the middle of the context sequence, for a total of 200 bp of context sequence surrounding each SNP.

File CD000018ORD_SEQLIST.TXT is 716 KB in size, and was created on Nov. 25, 2008.

The Sequence Listing is hereby incorporated by reference pursuant to 37 CFR 1.77(b)(4).

DESCRIPTION OF TABLE 1 AND TABLE 2

Table 1 and Table 2 disclose the SNP and associated gene/transcript/protein information of the present invention. For each gene, Table 1 provides a header containing gene, transcript and protein information, followed by a transcript and protein sequence identifier (SEQ ID NO), and then SNP information regarding each SNP found in that gene/transcript including the transcript context sequence. For each gene in Table 2, a header is provided that contains gene and genomic information, followed by a genomic sequence identifier (SEQ ID NO) and then SNP information regarding each SNP found in that gene, including the genomic context sequence.

Note that SNP markers may be included in both Table 1 and Table 2; Table 1 presents the SNPs relative to their transcript sequences and encoded protein sequences, whereas Table 2 presents the SNPs relative to their genomic sequences. In some instances Table 2 may also include, after the last gene sequence, genomic sequences of one or more intergenic regions, as well as SNP context sequences and other SNP information for any SNPs that lie within these intergenic regions. Additionally, in either Table 1 or 2 a “Related Interrogated SNP” may be listed following a SNP which is determined to be in LD with that interrogated SNP according to the given Power value. SNPs can be readily cross-referenced between all Tables based on their Celera hCV (or, in some instances, hDV) identification numbers and/or public rs identification numbers, and to the Sequence Listing based on their corresponding SEQ ID NOs.

The gene/transcript/protein information includes:

-   -   a gene number (1 through n, where n=the total number of genes in         the Table),     -   a gene symbol, along with an Entrez gene identification number         (Entrez Gene database, National Center for Biotechnology         Information (NCBI), National Library of Medicine, National         Institutes of Health)     -   a gene name,     -   an accession number for the transcript (e.g., RefSeq NM number,         or a Celera hCT identification number if no RefSeq NM number is         available) (Table 1 only),     -   an accession number for the protein (e.g., RefSeq NP number, or         a Celera hCP identification number if no RefSeq NP number is         available) (Table 1 only),     -   the chromosome number of the chromosome on which the gene is         located,     -   an OMIM (“Online Mendelian Inheritance in Man” database, Johns         Hopkins University/NCBI) public reference number for the gene,         and OMIM information such as alternative gene/protein name(s)         and/or symbol(s) in the OMIM entry.

Note that, due to the presence of alternative splice forms, multiple transcript/protein entries may be provided for a single gene entry in Table 1; i.e., for a single Gene Number, multiple entries may be provided in series that differ in their transcript/protein information and sequences.

Following the gene/transcript/protein information is a transcript context sequence (Table 1), or a genomic context sequence (Table 2), for each SNP within that gene.

After the last gene sequence, Table 2 may include additional genomic sequences of intergenic regions (in such instances, these sequences are identified as “Intergenic region:” followed by a numerical identification number), as well as SNP context sequences and other SNP information for any SNPs that lie within each intergenic region (such SNPs are identified as “INTERGENIC” for SNP type).

Note that the transcript, protein, and transcript-based SNP context sequences are all provided in the Sequence Listing. The transcript-based SNP context sequences are provided in both Table 1 and also in the Sequence Listing. The genomic and genomic-based SNP context sequences are provided in the Sequence Listing. The genomic-based SNP context sequences are provided in both Table 2 and in the Sequence Listing. SEQ ID NOs are indicated in Table 1 for the transcript-based context sequences (SEQ ID NOS:5-9); SEQ ID NOs are indicated in Table 2 for the genomic-based context sequences (SEQ ID NOS:19-110).

The SNP information includes:

-   -   Context sequence (taken from the transcript sequence in Table 1,         the genomic sequence in Table 2) with the SNP represented by its         IUB code, including 100 bp upstream (5′) of the SNP position         plus 100 bp downstream (3′) of the SNP position (the         transcript-based SNP context sequences in Table 1 are provided         in the Sequence Listing as SEQ ID NOS:5-9; the genomic-based SNP         context sequences in Table 2 are provided in the Sequence         Listing as SEQ ID NOS:19-110).     -   Celera hCV internal identification number for the SNP (in some         instances, an “hDV” number is given instead of an “hCV” number).     -   The corresponding public identification number for the SNP, the         rs number.     -   “SNP Chromosome Position” indicates the nucleotide position of         the SNP along the entire sequence of the chromosome as provided         in NCBI Genome Build 36.     -   SNP position (nucleotide position of the SNP within the given         transcript sequence (Table 1) or within the given genomic         sequence (Table 2)).     -   “Related Interrogated SNP” is the interrogated SNP with which         the listed SNP is in LD at the given value of Power.     -   SNP source (may include any combination of one or more of the         following five codes, depending on which internal sequencing         projects and/or public databases the SNP has been observed in:         “Applera”=SNP observed during the re-sequencing of genes and         regulatory regions of 39 individuals, “Celera”=SNP observed         during shotgun sequencing and assembly of the Celera human         genome sequence, “Celera Diagnostics”=SNP observed during         re-sequencing of nucleic acid samples from individuals who have         a disease, “dbSNP”=SNP observed in the dbSNP public database,         “HGBASE”=SNP observed in the HGBASE public database, “HGMD”=SNP         observed in the Human Gene Mutation Database (HGMD) public         database, “HapMap”=SNP observed in the International HapMap         Project public database, “CSNP”=SNP observed in an internal         Applied Biosystems (Foster City, Calif.) database of coding SNPS         (cSNPs).

Note that multiple “Applera” source entries for a single SNP indicate that the same SNP was covered by multiple overlapping amplification products and the re-sequencing results (e.g., observed allele counts) from each of these amplification products is being provided.

Certain SNPs from Tables 1 and 2 are SNPs for which the SNP source falls into one of the following three categories: 1) SNPs for which the SNP source is only “Applera” and none other, 2) SNPs for which the SNP source is only “Celera Diagnostics” and none other, and 3) SNPs for which the SNP source is both “Applera” and “Celera Diagnostics” but none other. These SNPs have not been observed in any of the public databases (dbSNP, HGBASE, and HGMD), and were also not observed during shotgun sequencing and assembly of the Celera human genome sequence (i.e., “Celera” SNP source). These SNPs include: hCV25597248 (transcript-based context sequence SEQ ID NO:39 in Table 1, genomic-based context sequence SEQ ID NO: 113 in Table 2) and hCV25635059 (transcript-based context sequences SEQ ID NOS:42 and 45 in Table 1; genomic-based context sequences SEQ ID NO:123 and 335 in Table 2).

-   -   Population/allele/allele count information in the format of         [population1(first_allele,countlsecond_allele,count)population2(first_allele,countlsecond_allele         count)total(first_allele,total countlsecond_allele,total         count)]. The information in this field includes         populations/ethnic groups in which particular SNP alleles have         been observed (“cau”=Caucasian, “his”=Hispanic, “chn”=Chinese,         and “afr”=African-American, “jpn”=Japanese, “ind”=Indian,         “mex”=Mexican, “ain”=“American Indian, “cra”=Celera donor,         “no_pop”=no population information available), identified SNP         alleles, and observed allele counts (within each population         group and total allele counts), where available [“−” in the         allele field represents a deletion allele of an         insertion/deletion (“indel”) polymorphism (in which case the         corresponding insertion allele, which may be comprised of one or         more nucleotides, is indicated in the allele field on the         opposite side of the “I”); “−” in the count field indicates that         allele count information is not available]. For certain SNPs         from the public dbSNP database, population/ethnic information is         indicated as follows (this population information is publicly         available in dbSNP): “HISP1”=human individual DNA (anonymized         samples) from 23 individuals of self-described HISPANIC         heritage; “PAC1”=human individual DNA (anonymized samples) from         24 individuals of self-described PACIFIC RIM heritage;         “CAUC1”=human individual DNA (anonymized samples) from 31         individuals of self-described CAUCASIAN heritage; “AFR1”=human         individual DNA (anonymized samples) from 24 individuals of         self-described AFRICAN/AFRICAN AMERICAN heritage; “P1”=human         individual DNA (anonymized samples) from 102 individuals of         self-described heritage; “PA130299515”; “SC_(—)12_A”=SANGER 12         DNAs of Asian origin from Corielle cell repositories, 6 of which         are male and 6 female; “SC_(—)12_C”=SANGER 12 DNAs of Caucasian         origin from Corielle cell repositories from the CEPH/UTAH         library, six male and six female; “SC_(—)12_AA”=SANGER 12 DNAs         of African-American origin from Corielle cell repositories 6 of         which are male and 6 female; “SC_(—)95_C”=SANGER 95 DNAs of         Caucasian origin from Corielle cell repositories from the         CEPH/UTAH library; and “SC_(—)12_CA”=Caucasians-12 DNAs from         Corielle cell repositories that are from the CEPH/UTAH library,         six male and six female.

Note that for SNPs of “Applera” SNP source, genes/regulatory regions of 39 individuals (20 Caucasians and 19 African Americans) were re-sequenced and, since each SNP position is represented by two chromosomes in each individual (with the exception of SNPs on X and Y chromosomes in males, for which each SNP position is represented by a single chromosome), up to 78 chromosomes were genotyped for each SNP position. Thus, the sum of the African-American (“afr”) allele counts is up to 38, the sum of the Caucasian allele counts (“cau”) is up to 40, and the total sum of all allele counts is up to 78.

Note that semicolons separate population/allele/count information corresponding to each indicated SNP source; i.e., if four SNP sources are indicated, such as “Celera,” “dbSNP,” “HGBASE,” and “HGMD,” then population/allele/count information is provided in four groups which are separated by semicolons and listed in the same order as the listing of SNP sources, with each population/allele/count information group corresponding to the respective SNP source based on order; thus, in this example, the first population/allele/count information group would correspond to the first listed SNP source (Celera) and the third population/allele/count information group separated by semicolons would correspond to the third listed SNP source (HGBASE); if population/allele/count information is not available for any particular SNP source, then a pair of semicolons is still inserted as a place-holder in order to maintain correspondence between the list of SNP sources and the corresponding listing of population/allele/count information.

SNP type (e.g., location within gene/transcript and/or predicted functional effect) [“MIS-SENSE MUTATION”=SNP causes a change in the encoded amino acid (i.e., a non-synonymous coding SNP); “SILENT MUTATION”=SNP does not cause a change in the encoded amino acid (i.e., a synonymous coding SNP); “STOP CODON MUTATION”=SNP is located in a stop codon; “NONSENSE MUTATION”=SNP creates or destroys a stop codon; “UTR 5”=SNP is located in a 5′ UTR of a transcript; “UTR 3”=SNP is located in a 3′ UTR of a transcript; “PUTATIVE UTR 5”=SNP is located in a putative 5′ UTR; “PUTATIVE UTR 3”=SNP is located in a putative 3′ UTR; “DONOR SPLICE SITE”=SNP is located in a donor splice site (5′ intron boundary); “ACCEPTOR SPLICE SITE”=SNP is located in an acceptor splice site (3′ intron boundary); “CODING REGION”=SNP is located in a protein-coding region of the transcript; “EXON”=SNP is located in an exon; “INTRON”=SNP is located in an intron; “hmCS”=SNP is located in a human-mouse conserved segment; “TFBS”=SNP is located in a transcription factor binding site; “UNKNOWN”=SNP type is not defined; “INTERGENIC”=SNP is intergenic, i.e., outside of any gene boundary].

Protein coding information (Table 1 only), where relevant, in the format of [protein SEQ ID NO, amino acid position, (amino acid-1, codon1) (amino acid-2, codon2)]. The information in this field includes SEQ ID NO of the encoded protein sequence, position of the amino acid residue within the protein identified by the SEQ ID NO that is encoded by the codon containing the SNP, amino acids (represented by one-letter amino acid codes) that are encoded by the alternative SNP alleles (in the case of stop codons, “X” is used for the one-letter amino acid code), and alternative codons containing the alternative SNP nucleotides which encode the amino acid residues (thus, for example, for missense mutation-type SNPs, at least two different amino acids and at least two different codons are generally indicated; for silent mutation-type SNPs, one amino acid and at least two different codons are generally indicated, etc.). In instances where the SNP is located outside of a protein-coding region (e.g., in a UTR region), “None” is indicated following the protein SEQ ID NO.

Note that SNPs can be cross-referenced between all tables herein based on the hCV/hDV and/or rs identification number of each SNP.

DESCRIPTION OF TABLE 3

Table 3 provides sequences (SEQ ID NOS: 111-164) of primers that have been synthesized and used in the laboratory to assay certain SNPs by allele-specific PCR during the course of association studies to verify the association of these SNPs with psoriasis (see Examples section).

Table 3 provides the following:

-   -   the column labeled “Marker” provides an hCV identification         number for each SNP.     -   the column labeled “rs number” provides the public rs         identification number for each SNP.     -   the column labeled “Alleles” designates the two alternative         alleles (i.e., nucleotides) at the SNP site. These alleles are         targeted by the allele-specific primers (the allele-specific         primers are shown as Primer I and Primer 2). Note that alleles         may be presented in Table 3 based on a different orientation         (i.e., the reverse complement) relative to how the same alleles         are presented in Tables 1-2.     -   the column labeled “Primer 1 (Allele-Specific Primer)” provides         an allele-specific primer that is specific for an allele         designated in the “Alleles” column.     -   the column labeled “Primer 2 (Allele-Specific Primer)” provides         an allele-specific primer that is specific for the other allele         designated in the “Alleles” column.     -   the column labeled “Common Primer” provides a common primer that         is used in conjunction with each of the allele-specific primers         (i.e., Primer I and Primer 2) and which hybridizes at a site         away from the SNP position.

All primer sequences are given in the 5′ to 3′ direction.

Each of the nucleotides designated in the “Alleles” column matches or is the reverse complement of (depending on the orientation of the primer relative to the designated allele) the 3′ nucleotide of the allele-specific primer (i.e., either Primer 1 or Primer 2) that is specific for that allele.

DESCRIPTION OF TABLE 4

Table 4 provides a list of LD SNPs that are related to and derived from certain interrogated SNPs. The interrogated SNPs, which are shown in column 1 (which indicates the hCV identification numbers of each interrogated SNP) and column 2 (which indicates the public rs identification numbers of each interrogated SNP) of Table 4, are statistically significantly associated with psoriasis as shown in the tables. These LD SNPs are provided as an example of SNPs which can also serve as markers for disease association based on their being in LD with an interrogated SNP. The criteria and process of selecting such LD SNPs, including the calculation of the r² value and the r² threshold value, are described in Example Two, below.

In Table 4, the column labeled “Interrogated SNP” presents each marker as identified by its unique hCV identification number. The column labeled “Interrogated rs” presents the publicly known identifier rs number for the corresponding hCV number. The column labeled “LD SNP” presents the hCV numbers of the LD SNPs that are derived from their corresponding interrogated SNPs. The column labeled “LD SNP rs” presents the publicly known rs number for the corresponding hCV number. The column labeled “Power” presents the level of power where the r² threshold is set. For example, when power is set at 0.51, the threshold r² value calculated therefrom is the minimum r² that an LD SNP must have in reference to an interrogated SNP, in order for the LD SNP to be classified as a marker capable of being associated with a disease phenotype at greater than 51% probability. The column labeled “Threshold r2” presents the minimum value of r² that an LD SNP must meet in reference to an interrogated SNP in order to qualify as an LD SNP. The column labeled “r²” presents the actual r² value of the LD SNP in reference to the interrogated SNP to which it is related.

DESCRIPTIONS OF TABLES 5-16

Table 5 provides statistical results for the association of the IL13 SNPs rs1800925, rs20541, and rs848 with psoriasis. All three SNPs were individually genotyped using allele-specific real-time PCR as previously described⁹. Positions for each SNP are given according to genomic contig NT_(—)034772.5 (Entrez Nucleotide). The minor allele is listed first, followed by the position in National Center for Biotechnology Information Genome Build 36.2 and then the major allele. All samples were white of Northern European extraction and are described in detail elsewhere⁹ (Sample set 1: 467 cases/460 controls; sample set 2: 498 cases/498 controls; sample set 3: 481 cases/424 controls). Hardy-Weinberg exact P-values were calculated according to the formula of Weir.¹⁰ Fisher's exact P-values were calculated for allelic data. Two-tailed P-values are presented for Sample Set 1; one-tailed P-values are given for Sample Sets 2 and 3. Combined P-values were calculated using a Fisher's combined P-value, or omnibus method. Genotypic P-values for the individual sample sets were calculated using the William's-corrected G test.¹¹ As with the allelic analysis, Sample Set 1 P-values are two-tailed; Sample Set 2 and 3 P-values are one-tailed. Odds ratios were calculated for the minor allele. Combined or joint ORs were calculated using a Mantel-Haenszel common OR.

Table 6 provides statistical results for the association of three-marker IL13 haplotypes with psoriasis. The order of SNPs is rs1800925-rs20541-rs848. The Haplo.Stats package was used to estimate haplotype frequencies from unphased data, treating cases and controls separately, and to test for association between haplotypes and disease status. Global P-values were also calculated and all P-values were adjusted for haplotype estimation error. Two tailed P-values are presented for sample set 1; one-tailed P-values are presented for Sample Sets 2 and 3. P-values were combined using Fisher's combined probability method. Mantel-Haenszel joint odds ratios (OR_(MH)) were calculated for each haplotype.

Table 7 provides HLA-cytokine pathway psoriasis relative risk estimates. HLA-C genotypes were partitioned into *0602 carriers and noncarriers. IL12B (rs3212227 and 6887695) and IL23R (rs7530511 and rs11209026) diplotypes⁹ were combined with IL13 (rs1800925) genotypes and classified into risk groups (Very High to Low) as outlined below Table 7. Psoriasis prevalence was set at 0.03. The probability of psoriasis conditioned on each multi-locus genotype —P(MLG), was calculated using Bayes' theorem. The P(MLG) was divided by the unconditioned psoriasis risk (disease prevalence) to obtain the relative risk values. Confidence intervals were calculated using 10,000 replicates of a Monte Carlo simulation. The scaled relative risk (SRR) was calculated by setting the lowest risk category equal to 1.

Throughout Tables 5-16, “OR” refers to the odds ratio, “95% Cl” refers to the 95% confidence interval for the odds ratio, “MAF” refers to the minor allele frequency, and “HWE” refers to Hardy-Weinberg equilibrium.

Odds ratios (OR) greater than one indicate that a given allele (or combination of alleles such as a haplotype, diplotype, or two-locus diplotype) is a risk allele, whereas odds ratios less than one indicate that a given allele is a non-risk allele (which may also be referred to as a protective allele). For a given risk (i.e., susceptible) allele, the other alternative allele at the SNP position (which can be derived from the information provided in Tables 1-2, for example) may be considered a non-risk (i.e., protective) allele. For a given non-risk (i.e., protective) allele, the other alternative allele at the SNP position may be considered a risk allele.

Tables 8-16 are described in detail in the Examples section below.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a HLA-cytokine pathway psoriasis relative risk plot. The scaled relative risk of each multi-locus genotype combination is graphed using the data from sample set 1. HLA-C genotypes were determined as previously described⁹ and aggregated into *0602 carriers and noncarriers. Psoriasis-associated diplotypes at IL12B (rs3212227 and rs6887695) and IL23R (rs753511 and rs11209026)₉ were combined with genotypes at rs1800925 (5′ of IL13) and partitioned into four cytokine pathway categories: very high risk, high risk, moderate risk and low risk (as described in the description of Table 7). Psoriasis prevalence of 0.03 was assumed. Conditional independence between the loci was also assumed [Three separate analyses—a genotype-based squared correlation coefficient, the Multifactor dimensionality reduction (MDR) program,¹⁷ and a Monte Carlo simulation—substantiated this assumption and provided no evidence for significant interaction effects between these markers.]. Expected frequencies of the multi-locus genotype combinations are presented for both affected and unaffected populations and are listed above each bar (affected/unaffected). The relative risk estimates were scaled with respect to the lowest risk category, which was set at 1.00.

FIG. 2 shows a schematic representation of (A) P-values of SNPs tested in the three sample sets individually (SS1, SS2 and SS3) and combined (meta). A total of 93 SNPs were tested in sample set 1; 12 SNPs were tested in sample sets 2 and 3. rs1800925, rs20541 and rs848, which were previously reported to be associated with psoriasis (21), were tested in all three sample sets. A gene map is shown at the bottom, based on the NCBI b36 genome assembly. (B) Inter-marker LD of the 93 markers in sample set 1. D′ values are shown in the top right triangle, and r² values are in the bottom triangle. The D′ and r² values were calculated using both cases and controls of sample set 1.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides SNPs associated with psoriasis and related pathologies, nucleic acid molecules containing SNPs, methods and reagents for the detection of the SNPs disclosed herein, uses of these SNPs for the development of detection reagents, and assays or kits that utilize such reagents. The psoriasis-associated SNPs disclosed herein are useful for diagnosing, screening for, and evaluating predisposition to psoriasis, including an increased or decreased risk of developing psoriasis, the rate of progression of psoriasis, and related pathologies in humans. Furthermore, such SNPs and their encoded products are useful targets for the development of therapeutic agents.

A large number of SNPs have been identified from re-sequencing DNA from 39 individuals, and they are indicated as “Applera” SNP source in Tables 1-2. Their allele frequencies observed in each of the Caucasian and African-American ethnic groups are provided. Additional SNPs included herein were previously identified during shotgun sequencing and assembly of the human genome, and they are indicated as “Celera” SNP source in Tables 1-2. Furthermore, the information provided in Table 1-2, particularly the allele frequency information obtained from 39 individuals and the identification of the precise position of each SNP within each gene/transcript, allows haplotypes (i.e., groups of SNPs that are co-inherited) to be readily inferred. The present invention encompasses SNP haplotypes, as well as individual SNPs.

Thus, the present invention provides individual SNPs associated with psoriasis, as well as combinations of SNPs and haplotypes in genetic regions associated with psoriasis, polymorphic/variant transcript sequences (SEQ ID NOS:1-2) and genomic sequences (SEQ ID NOS:10-18) containing SNPs, encoded amino acid sequences (SEQ ID NOS: 3-4), and both transcript-based SNP context sequences (SEQ ID NOS: 5-9) and genomic-based SNP context sequences (SEQ ID NOS:19-110) (transcript sequences, protein sequences, and transcript-based SNP context sequences are provided in Table 1 and the Sequence Listing; genomic sequences and genomic-based•SNP context sequences are provided in Table 2 and the Sequence Listing), methods of detecting these polymorphisms in a test sample, methods of determining the risk of an individual of having or developing psoriasis, methods of screening for compounds useful for treating disorders associated with a variant gene/protein such as psoriasis, compounds identified by these screening methods, methods of using the disclosed SNPs to select a treatment strategy, methods of treating a disorder associated with a variant gene/protein (i.e., therapeutic methods), and methods of using the SNPs of the present invention for human identification.

The present invention provides novel SNPs associated with psoriasis and related pathologies, as well as SNPs that were previously known in the art, but were not previously known to be associated with psoriasis. Accordingly, the present invention provides novel compositions and methods based on the novel SNPs disclosed herein, and also provides novel methods of using the known, but previously unassociated, SNPs in methods relating to psoriasis (e.g., for diagnosing psoriasis, etc.). In Tables 1-2, known SNPs are identified based on the public database in which they have been observed, which is indicated as one or more of the following SNP types: “dbSNP”=SNP observed in dbSNP, “HGBASE”=SNP observed in HGBASE, and “HGMD”=SNP observed in the Human Gene Mutation Database (HGMD). Novel SNPs for which the SNP source is only “Applera” and none other, i.e., those that have not been observed in any public databases and which were also not observed during shotgun sequencing and assembly of the Celera human genome sequence (i.e., “Celera” SNP source), are indicated in the tables.

Particular SNP alleles of the present invention can be associated with either an increased risk of having or developing psoriasis and related pathologies, or a decreased risk of having or developing psoriasis. SNP alleles that are associated with a decreased risk of having or developing psoriasis may be referred to as “protective” alleles, and SNP alleles that are associated with an increased risk of having or developing psoriasis may be referred to as “susceptibility” alleles, “risk” alleles, or “risk factors”. Thus, whereas certain SNPs (or their encoded products) can be assayed to determine whether an individual possesses a SNP allele that is indicative of an increased risk of having or developing psoriasis (i.e., a susceptibility allele), other SNPs (or their encoded products) can be assayed to determine whether an individual possesses a SNP allele that is indicative of a decreased risk of having or developing psoriasis (i.e., a protective allele). Similarly, particular SNP alleles of the present invention can be associated with either an increased or decreased likelihood of responding to a particular treatment or therapeutic compound, or an increased or decreased likelihood of experiencing toxic effects from a particular treatment or therapeutic compound. The term “altered” may be used herein to encompass either of these two possibilities (e.g., an increased or a decreased risk/likelihood).

Those skilled in the art will readily recognize that nucleic acid molecules may be double-stranded molecules and that reference to a particular site on one strand refers, as well, to the corresponding site on a complementary strand. In defining a SNP position, SNP allele, or nucleotide sequence, reference to an adenine, a thymine (uridine), a cytosine, or a guanine at a particular site on one strand of a nucleic acid molecule also defines the thymine (uridine), adenine, guanine, or cytosine (respectively) at the corresponding site on a complementary strand of the nucleic acid molecule. Thus, reference may be made to either strand in order to refer to a particular SNP position, SNP allele, or nucleotide sequence. Probes and primers, may be designed to hybridize to either strand and SNP genotyping methods disclosed herein may generally target either strand. Throughout the specification, in identifying a SNP position, reference is generally made to the protein-encoding strand, only for the purpose of convenience.

References to variant peptides, polypeptides, or proteins of the present invention include peptides, polypeptides, proteins, or fragments thereof, that contain at least one amino acid residue that differs from the corresponding amino acid sequence of the art-known peptide/polypeptide/protein (the art-known protein may be interchangeably referred to as the “wild-type”, “reference”, or “normal” protein). Such variant peptides/polypeptides/proteins can result from a codon change caused by a nonsynonymous nucleotide substitution at a protein-coding SNP position (i.e., a missense mutation) disclosed by the present invention. Variant peptides/polypeptides/proteins of the present invention can also result from a nonsense mutation, i.e., a SNP that creates a premature stop codon, a SNP that generates a read-through mutation by abolishing a stop codon, or due to any SNP disclosed by the present invention that otherwise alters the structure, function/activity, or expression of a protein, such as a SNP in a regulatory region (e.g. a promoter or enhancer) or a SNP that leads to alternative or defective splicing, such as a SNP in an intron or a SNP at an exon/intron boundary. As used herein, the terms “polypeptide”, “peptide”, and “protein” are used interchangeably.

IL12B, IL23R, and HLA-C

The SNPs provided herein, particularly rs1800925, rs20541, and rs848 [including the risk haplotype of rs1800925(C), rs20541(C), and rs848(G); and the protective haplotype of rs1800925(T), rs20541(T), and rs848(T)], can be combined with other genetic variants, such as to increase the power to determine psoriasis risk. In certain preferred embodiments, SNPs rs1800925, rs20541, and rs848 are combined with any or all of HLA-C (e.g., HLA-C variant *0602), IL12B SNPs, and IL23 SNPs.

Exemplary IL12B and IL23R variants which can be used in conjunction with rs1800925, rs20541, and/or rs848 include any of the following SNPs, haplotypes, diplotypes, and two-locus diplotypes, and any combination of these:

SNPs rs321227, rs3212220, rs7709212, and rs6887695.

The psoriasis risk haplotype rs3212227(A) and rs6887695(G).

The psoriasis protective haplotype rs3212227(C) and rs6887695(C).

The psoriasis risk haplotype rs11209026(G) and rs7530511(C).

A SNP diplotype which is a combination of two copies of the risk haplotype, rs3212227(A)/rs6887695(G).

A SNP diplotype which is a combination of two copies of the protective haplotype, rs3212227(C)/rs6887695(C).

A SNP diplotype which is a combination of two copies of the risk haplotype rs11209026(G)/rs 7530511 (C).

A two-locus diplotype which is a combination of rs3212227(A)/rs6887695(G)/rs11209026(G)/rs7530511(C).

Further information regarding the above IL12B and IL23R SNPs, haplotypes, and diplotypes, including allele information and exemplary nucleotide sequences, is disclosed in Cargill et al., “A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis risk genes”, Am J Hum Genet. 2007 February; 80(2):273-90, which is incorporated herein by reference.

IL13 Therapeutics/Pharmacogenomics in Inflammatory and Autoimmune Disorders

Exemplary embodiments of the invention provide SNPs in IL13 that are associated with psoriasis (as shown in the tables). These SNPs have a variety of therapeutic and pharmacogenomic uses related to the treatment of psoriasis, as well as other inflammatory and autoimmune disorders such as Crohn's disease and bronchial hyperresponsiveness/atopy/asthma. The psoriasis-associated SNPs provided herein may be used, for example, to determine variability between different individuals in their response to an inflammatory or autoimmune disease therapy (e.g., a psoriasis therapy or a therapy for Crohn's disease, bronchial hyperresponsiveness/atopy/asthma, or other inflammatory or autoimmune disorder) such as to predict whether an individual will respond positively to a particular therapy, to determine the most effective therapeutic agent (e.g., antibody, small molecule compound, nucleic acid agent, etc.) to use to treat an individual, to determine whether a particular therapeutic agent should or should not be administered to an individual (e.g., by predicting whether the individual is likely to positively respond to the therapy or by predicting whether the individual will experience toxic or other other undesirable side effects or is unlikely to respond to the therapy), or to determine the therapeutic regimen to use for an individual such as the dosage or frequency of dosing of a therapeutic agent for a particular individual. Therapeutic agents that directly modulate IL13 may be used to treat psoriasis or other inflammatory/autoimmune disorders and, furthermore, therapeutic agents that target proteins that interact with IL13 or are otherwise in IL13 pathways may be used to indirectly modulate IL13 to thereby treat psoriasis or other inflammatory/autoimmune disorders. Any therapeutic agents such as these may be used in conjunction with the SNPs provided herein.

For example, the IL13 psoriasis-associated SNPs provided herein may be used to predict whether an individual will respond positively to anti-IL13 antibody therapy and/or to determine the most effective dosage of this therapy. This facilitates decision making by medical practitioners, such as in deciding whether to administer this therapy to a particular individual or select another therapy that may be better suited to the individual, or to use a particular dosage, dosing schedule, or to modify other aspects of a therapeutic regimen to effectively treat the individual, for example.

In addition to medical treatment, these uses may also be applied, for example, in the context of clinical trials of a therapeutic agent (e.g., a therapeutic agent that targets IL13 for the treatment of psoriasis, Crohn's disease, bronchial hyperresponsiveness/atopy/asthma, or other inflammatory or autoimmune disorder), such as to include particular individuals in a clinical trial who are predicted to positively respond to the therapeutic agent based on the SNPs provided herein and/or to exclude particular individuals from a clinical trial who are predicted to not positively respond to the therapeutic agent based on the SNPs provided herein. By using the SNPs provided herein to target a therapeutic agent to individuals who are more likely to positively respond to the agent, the therapeutic agent is more likely to succeed in clinical trials by showing positive efficacy and to therefore satisfy the FDA requirements for approval. Additionally, individuals who are more likely to experience toxic or other undesirable side effects may be excluded from being administered the therapeutic agent. Furthermore, by using the SNPs provided herein to determine an effective dosage or dosing frequency, for example, the therapeutic agent may be less likely to exhibit toxicity or other undesirable side effects, as well as more likely to achieve positive efficacy.

Isolated Nucleic Acid Molecules and SNP Detection Reagents & Kits

Tables 1 and 2 provide a variety of information about each SNP of the present invention that is associated with psoriasis, including the transcript sequences (SEQ ID NOS:1-2), genomic sequences (SEQ ID NOS:10-18), and protein sequences (SEQ ID NOS:3-4) of the encoded gene products (with the SNPs indicated by IUB codes in the nucleic acid sequences). In addition, Tables 1 and 2 include SNP context sequences, which generally include 100 nucleotide upstream (5′) plus 100 nucleotides downstream (3′) of each SNP position (SEQ ID NOS:5-9) correspond to transcript-based SNP context sequences disclosed in Table 1, and SEQ ID NOS:19-110 correspond to genomic-based context sequences disclosed in Table 2), the alternative nucleotides (alleles) at each SNP position, and additional information about the variant where relevant, such as SNP type (coding, missense, splice site, UTR, etc.), human populations in which the SNP was observed, observed allele frequencies, information about the encoded protein, etc.

Isolated Nucleic Acid Molecules

The present invention provides isolated nucleic acid molecules that contain one or more SNPs disclosed Table 1 and/or Table 2. Preferred isolated nucleic acid molecules contain one or more SNPs identified as Applera or Celera proprietary. Isolated nucleic acid molecules containing one or more SNPs disclosed in at least one of Tables 1-2 may be interchangeably referred to throughout the present text as “SNP-containing nucleic acid molecules”. Isolated nucleic acid molecules may optionally encode a full-length variant protein or fragment thereof. The isolated nucleic acid molecules of the present invention also include probes and primers (which are described in greater detail below in the section entitled “SNP Detection Reagents”), which may be used for assaying the disclosed SNPs, and isolated full-length genes, transcripts, cDNA molecules, and fragments thereof, which may be used for such purposes as expressing an encoded protein.

As used herein, an “isolated nucleic acid molecule” generally is one that contains a SNP of the present invention or one that hybridizes to such molecule such as a nucleic acid with a complementary sequence, and is separated from most other nucleic acids present in the natural source of the nucleic acid molecule. Moreover, an “isolated” nucleic acid molecule, such as a cDNA molecule containing a SNP of the present invention, can be substantially free of other cellular material, or culture medium when produced by recombinant techniques, or chemical precursors or other chemicals when chemically synthesized. A nucleic acid molecule can be fused to other coding or regulatory sequences and still be considered “isolated”. Nucleic acid molecules present in non-human transgenic animals, which do not naturally occur in the animal, are also considered “isolated”. For example, recombinant DNA molecules contained in a vector are considered “isolated”. Further examples of “isolated” DNA molecules include recombinant DNA molecules maintained in heterologous host cells, and purified (partially or substantially) DNA molecules in solution. Isolated RNA molecules include in vivo or in vitro RNA transcripts of the isolated SNP-containing DNA molecules of the present invention. Isolated nucleic acid molecules according to the present invention further include such molecules produced synthetically.

Generally, an isolated SNP-containing nucleic acid molecule comprises one or more SNP positions disclosed by the present invention with flanking nucleotide sequences on either side of the SNP positions. A flanking sequence can include nucleotide residues that are naturally associated with the SNP site and/or heterologous nucleotide sequences. Preferably the flanking sequence is up to about 500, 300, 100, 60, 50, 30, 25, 20, 15, 10, 8, or 4 nucleotides (or any other length in-between) on either side of a SNP position, or as long as the full-length gene or entire protein-coding sequence (or any portion thereof such as an exon), especially if the SNP-containing nucleic acid molecule is to be used to produce a protein or protein fragment.

For full-length genes and entire protein-coding sequences, a SNP flanking sequence can be, for example, up to about 5 KB, 4 KB, 3 KB, 2 KB, 1 KB on either side of the SNP. Furthermore, in such instances, the isolated nucleic acid molecule comprises exonic sequences (including protein-coding and/or non-coding exonic sequences), but may also include intronic sequences. Thus, any protein coding sequence may be either contiguous or separated by introns. The important point is that the nucleic acid is isolated from remote and unimportant flanking sequences and is of appropriate length such that it can be subjected to the specific manipulations or uses described herein such as recombinant protein expression, preparation of probes and primers for assaying the SNP position, and other uses specific to the SNP-containing nucleic acid sequences.

An isolated SNP-containing nucleic acid molecule can comprise, for example, a full-length gene or transcript, such as a gene isolated from genomic DNA (e.g., by cloning or PCR amplification), a cDNA molecule, or an mRNA transcript molecule. Polymorphic transcript sequences are provided in Table 1 and in the Sequence Listing (SEQ ID NOS:1-2), and polymorphic genomic sequences are provided in Table 2 and in the Sequence Listing (SEQ ID NOS:10-18). Furthermore, fragments of such full-length genes and transcripts that contain one or more SNPs disclosed herein are also encompassed by the present invention, and such fragments may be used, for example, to express any part of a protein, such as a particular functional domain or an antigenic epitope.

Thus, the present invention also encompasses fragments of the nucleic acid sequences provided in Tables 1-2 (transcript sequences are provided in Table 1 as SEQ ID NOS:1-2, genomic sequences are provided in Table 2 as SEQ ID NOS:10-18, transcript-based SNP context sequences are provided in Table 1 as SEQ ID NO:5-9, and genomic-based SNP context sequences are provided in Table 2 as SEQ ID NO:19-110) and their complements. A fragment typically comprises a contiguous nucleotide sequence at least about 8 or more nucleotides, more preferably at least about 12 or more nucleotides, and even more preferably at least about 16 or more nucleotides. Further, a fragment could comprise at least about 18, 20, 22, 25, 30, 40, 50, 60, 80, 100, 150, 200, 250 or 500 (or any other number in-between) nucleotides in length. The length of the fragment will be based on its intended use. For example, the fragment can encode epitope-bearing regions of a variant peptide or regions of a variant peptide that differ from the normal/wild-type protein, or can be useful as a polynucleotide probe or primer. Such fragments can be isolated using the nucleotide sequences provided in Table 1 and/or Table 2 for the synthesis of a polynucleotide probe. A labeled probe can then be used, for example, to screen a cDNA library, genomic DNA library, or mRNA to isolate nucleic acid corresponding to the coding region. Further, primers can be used in amplification reactions, such as for purposes of assaying one or more SNPs sites or for cloning specific regions of a gene.

An isolated nucleic acid molecule of the present invention further encompasses a SNP-containing polynucleotide that is the product of any one of a variety of nucleic acid amplification methods, which are used to increase the copy numbers of a polynucleotide of interest in a nucleic acid sample. Such amplification methods are well known in the art, and they include but are not limited to, polymerase chain reaction (PCR) (U.S. Pat. Nos. 4,683,195; and 4,683,202; PCR Technology: Principles and Applications for DNA Amplification, ed. H. A. Erlich, Freeman Press, NY, N.Y., 1992), ligase chain reaction (LCR) (Wu and Wallace, Genomics 4:560, 1989; Landegren et al., Science 241:1077, 1988), strand displacement amplification (SDA) (U.S. Pat. Nos. 5,270,184; and 5,422,252), transcription-mediated amplification (TMA) (U.S. Pat. No. 5,399,491), linked linear amplification (LLA) (U.S. Pat. No. 6,027,923), and the like, and isothermal amplification methods such as nucleic acid sequence based amplification (NASBA), and self-sustained sequence replication (Guatelli et al., Proc. Natl. Acad. Sci. USA 87: 1874, 1990). Based on such methodologies, a person skilled in the art can readily design primers in any suitable regions 5′ and 3′ to a SNP disclosed herein. Such primers may be used to amplify DNA of any length so long that it contains the SNP of interest in its sequence.

As used herein, an “amplified polynucleotide” of the invention is a SNP-containing nucleic acid molecule whose amount has been increased at least two fold by any nucleic acid amplification method performed in vitro as compared to its starting amount in a test sample. In other preferred embodiments, an amplified polynucleotide is the result of at least ten fold, fifty fold, one hundred fold, one thousand fold, or even ten thousand fold increase as compared to its starting amount in a test sample. In a typical PCR amplification, a polynucleotide of interest is often amplified at least fifty thousand fold in amount over the unamplified genomic DNA, but the precise amount of amplification needed for an assay depends on the sensitivity of the subsequent detection method used.

Generally, an amplified polynucleotide is at least about 16 nucleotides in length. More typically, an amplified polynucleotide is at least about 20 nucleotides in length. In a preferred embodiment of the invention, an amplified polynucleotide is at least about 30 nucleotides in length. In a more preferred embodiment of the invention, an amplified polynucleotide is at least about 32, 40, 45, 50, or 60 nucleotides in length. In yet another preferred embodiment of the invention, an amplified polynucleotide is at least about 100, 200, 300, 400, or 500 nucleotides in length. While the total length of an amplified polynucleotide of the invention can be as long as an exon, an intron or the entire gene where the SNP of interest resides, an amplified product is typically up to about 1,000 nucleotides in length (although certain amplification methods may generate amplified products greater than 1000 nucleotides in length). More preferably, an amplified polynucleotide is not greater than about 600-700 nucleotides in length. It is understood that irrespective of the length of an amplified polynucleotide, a SNP of interest may be located anywhere along its sequence.

In a specific embodiment of the invention, the amplified product is at least about 201 nucleotides in length, comprises one of the transcript-based context sequences or the genomic-based context sequences shown in Tables 1-2. Such a product may have additional sequences on its 5′ end or 3′ end or both. In another embodiment, the amplified product is about 101 nucleotides in length, and it contains a SNP disclosed herein. Preferably, the SNP is located at the middle of the amplified product (e.g., at position 101 in an amplified product that is 201 nucleotides in length, or at position 51 in an amplified product that is 101 nucleotides in length), or within 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, or 20 nucleotides from the middle of the amplified product (however, as indicated above, the SNP of interest may be located anywhere along the length of the amplified product).

The present invention provides isolated nucleic acid molecules that comprise, consist of, or consist essentially of one or more polynucleotide sequences that contain one or more SNPs disclosed herein, complements thereof, and SNP-containing fragments thereof.

Accordingly, the present invention provides nucleic acid molecules that consist of any of the nucleotide sequences shown in Table 1 and/or Table 2 (transcript sequences are provided in Table 1 as SEQ ID NOS:1-2, genomic sequences are provided in Table 2 as SEQ ID NOS:10-18, transcript-based SNP context sequences are provided in Table 1 as SEQ ID NO:5-9, and genomic-based SNP context sequences are provided in Table 2 as SEQ ID NO:19-110), or any nucleic acid molecule that encodes any of the variant proteins provided in Table 1 (SEQ ID NOS:3-4). A nucleic acid molecule consists of a nucleotide sequence when the nucleotide sequence is the complete nucleotide sequence of the nucleic acid molecule.

The present invention further provides nucleic acid molecules that consist essentially of any of the nucleotide sequences shown in Table 1 and/or Table 2 (transcript sequences are provided in Table 1 as SEQ ID NOS:1-2, genomic sequences are provided in Table 2 as SEQ ID NOS:10-18, transcript-based SNP context sequences are provided in Table 1 as SEQ ID NO:5-9, and genomic-based SNP context sequences are provided in Table 2 as SEQ ID NO:19-110), or any nucleic acid molecule that encodes any of the variant proteins provided in Table 1 (SEQ ID NOS:3-4). A nucleic acid molecule consists essentially of a nucleotide sequence when such a nucleotide sequence is present with only a few additional nucleotide residues in the final nucleic acid molecule.

The present invention further provides nucleic acid molecules that comprise any of the nucleotide sequences shown in Table 1 and/or Table 2 or a SNP-containing fragment thereof (transcript sequences are provided in Table 1 as SEQ ID NOS:1-2, genomic sequences are provided in Table 2 as SEQ ID NOS:10-18, transcript-based SNP context sequences are provided in Table 1 as SEQ ID NO:5-9, and genomic-based SNP context sequences are provided in Table 2 as SEQ ID NO:19-110), or any nucleic acid molecule that encodes any of the variant proteins provided in Table I (SEQ ID NOS:3-4). A nucleic acid molecule comprises a nucleotide sequence when the nucleotide sequence is at least part of the final nucleotide sequence of the nucleic acid molecule. In such a fashion, the nucleic acid molecule can be only the nucleotide sequence or have additional nucleotide residues, such as residues that are naturally associated with it or heterologous nucleotide sequences. Such a nucleic acid molecule can have one to a few additional nucleotides or can comprise many more additional nucleotides. A brief description of how various types of these nucleic acid molecules can be readily made and isolated is provided below, and such techniques are well known to those of ordinary skill in the art (Sambrook and Russell, 2000, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, NY).

The isolated nucleic acid molecules can encode mature proteins plus additional amino or carboxyl-terminal amino acids or both, or amino acids interior to the mature peptide (when the mature form has more than one peptide chain, for instance). Such sequences may play a role in processing of a protein from precursor to a mature form, facilitate protein trafficking, prolong or shorten protein half-life, or facilitate manipulation of a protein for assay or production. As generally is the case in situ, the additional amino acids may be processed away from the mature protein by cellular enzymes.

Thus, the isolated nucleic acid molecules include, but are not limited to, nucleic acid molecules having a sequence encoding a peptide alone, a sequence encoding a mature peptide and additional coding sequences such as a leader or secretory sequence (e.g., a pre-pro or pro-protein sequence), a sequence encoding a mature peptide with or without additional coding sequences, plus additional non-coding sequences, for example introns and non-coding 5′ and 3′ sequences such as transcribed but untranslated sequences that play a role in, for example, transcription, mRNA processing (including splicing and polyadenylation signals), ribosome binding, and/or stability of mRNA. In addition, the nucleic acid molecules may be fused to heterologous marker sequences encoding, for example, a peptide that facilitates purification.

Isolated nucleic acid molecules can be in the form of RNA, such as mRNA, or in the form DNA, including cDNA and genomic DNA, which may be obtained, for example, by molecular cloning or produced by chemical synthetic techniques or by a combination thereof (Sambrook and Russell, 2000, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, NY). Furthermore, isolated nucleic acid molecules, particularly SNP detection reagents such as probes and primers, can also be partially or completely in the form of one or more types of nucleic acid analogs, such as peptide nucleic acid (PNA) (U.S. Pat. Nos. 5,539,082; 5,527,675; 5,623,049; 5,714,331). The nucleic acid, especially DNA, can be double-stranded or single-stranded. Single-stranded nucleic acid can be the coding strand (sense strand) or the complementary non-coding strand (anti-sense strand). DNA, RNA, or PNA segments can be assembled, for example, from fragments of the human genome (in the case of DNA or RNA) or single nucleotides, short oligonucleotide linkers, or from a series of oligonucleotides, to provide a synthetic nucleic acid molecule. Nucleic acid molecules can be readily synthesized using the sequences provided herein as a reference; oligonucleotide and PNA oligomer synthesis techniques are well known in the art (see, e.g., Corey, “Peptide nucleic acids: expanding the scope of nucleic acid recognition”, Trends Biotechnol. 1997 June; 15(6):224-9, and Hyrup et al., “Peptide nucleic acids (PNA): synthesis, properties and potential applications”, Bioorg Med. Chem. 1996 January; 4(1):5-23). Furthermore, large-scale automated oligonucleotide/PNA synthesis (including synthesis on an array or bead surface or other solid support) can readily be accomplished using commercially available nucleic acid synthesizers, such as the Applied Biosystems (Foster City, Calif.) 3900 High-Throughput DNA Synthesizer or Expedite 8909 Nucleic Acid Synthesis System, and the sequence information provided herein.

The present invention encompasses nucleic acid analogs that contain modified, synthetic, or non-naturally occurring nucleotides or structural elements or other alternative/modified nucleic acid chemistries known in the art. Such nucleic acid analogs are useful, for example, as detection reagents (e.g., primers/probes) for detecting one or more SNPs identified in Table 1 and/or Table 2. Furthermore, kits/systems (such as beads, arrays, etc.) that include these analogs are also encompassed by the present invention. For example, PNA oligomers that are based on the polymorphic sequences of the present invention are specifically contemplated. PNA oligomers are analogs of DNA in which the phosphate backbone is replaced with a peptide-like backbone (Lagriffoul et al., Bioorganic & Medicinal Chemistry Letters, 4: 1081-1082 (1994), Petersen et al., Bioorganic & Medicinal Chemistry Letters, 6: 793-796 (1996), Kumar et al., Organic Letters 3(9): 1269-1272 (2001), WO96/04000). PNA hybridizes to complementary RNA or DNA with higher affinity and specificity than conventional oligonucleotides and oligonucleotide analogs. The properties of PNA enable novel molecular biology and biochemistry applications unachievable with traditional oligonucleotides and peptides.

Additional examples of nucleic acid modifications that improve the binding properties and/or stability of a nucleic acid include the use of base analogs such as inosine, intercalators (U.S. Pat. No. 4,835,263) and the minor groove binders (U.S. Pat. No. 5,801,115). Thus, references herein to nucleic acid molecules, SNP-containing nucleic acid molecules, SNP detection reagents (e.g., probes and primers), oligonucleotides/polynucleotides include PNA oligomers and other nucleic acid analogs. Other examples of nucleic acid analogs and alternative/modified nucleic acid chemistries known in the art are described in Current Protocols in Nucleic Acid Chemistry, John Wiley & Sons, N.Y. (2002).

The present invention further provides nucleic acid molecules that encode fragments of the variant polypeptides disclosed herein as well as nucleic acid molecules that encode obvious variants of such variant polypeptides. Such nucleic acid molecules may be naturally occurring, such as paralogs (different locus) and orthologs (different organism), or may be constructed by recombinant DNA methods or by chemical synthesis. Non-naturally occurring variants may be made by mutagenesis techniques, including those applied to nucleic acid molecules, cells, or organisms. Accordingly, the variants can contain nucleotide substitutions, deletions, inversions and insertions (in addition to the SNPs disclosed in Tables 1-2). Variation can occur in either or both the coding and non-coding regions. The variations can produce conservative and/or non-conservative amino acid substitutions.

Further variants of the nucleic acid molecules disclosed in Tables 1-2, such as naturally occurring allelic variants (as well as orthologs and paralogs) and synthetic variants produced by mutagenesis techniques, can be identified and/or produced using methods well known in the art. Such further variants can comprise a nucleotide sequence that shares at least 70-80%, 80-85%, 85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity with a nucleic acid sequence disclosed in Table 1 and/or Table 2 (or a fragment thereof) and that includes a novel SNP allele disclosed in Table 1 and/or Table 2. Further, variants can comprise a nucleotide sequence that encodes a polypeptide that shares at least 70-80%, 80-85%, 85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity with a polypeptide sequence disclosed in Table 1 (or a fragment thereof) and that includes a novel SNP allele disclosed in Table 1 and/or Table 2. Thus, an aspect of the present invention that is specifically contemplated are isolated nucleic acid molecules that have a certain degree of sequence variation compared with the sequences shown in Tables 1-2, but that contain a novel SNP allele disclosed herein. In other words, as long as an isolated nucleic acid molecule contains a novel SNP allele disclosed herein, other portions of the nucleic acid molecule that flank the novel SNP allele can vary to some degree from the specific transcript, genomic, and context sequences shown in Tables 1-2, and can encode a polypeptide that varies to some degree from the specific polypeptide sequences shown in Table 1.

To determine the percent identity of two amino acid sequences or two nucleotide sequences of two molecules that share sequence homology, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non-homologous sequences can be disregarded for comparison purposes). In a preferred embodiment, at least 30%, 40%, 50%, 60%, 70%, 80%, or 90% or more of the length of a reference sequence is aligned for comparison purposes. The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide as the corresponding position in the second sequence, then the molecules are identical at that position (as used herein, amino acid or nucleic acid “identity” is equivalent to amino acid or nucleic acid “homology”). The percent identity between the two sequences is a function of the number of identical positions shared by the sequences, taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences.

The comparison of sequences and determination of percent identity between two sequences can be accomplished using a mathematical algorithm. (Computational Molecular Biology, Lesk, A. M., ed., Oxford University Press, New York, 1988; Biocomputing: Informatics and Genome Projects, Smith, D. W., ed., Academic Press, New York, 1993; Computer Analysis of Sequence Data, Part 1, Griffin, A. M., and Griffin, H. G., eds., Humana Press, New Jersey, 1994; Sequence Analysis in Molecular Biology, von Heinje, G., Academic Press, 1987; and Sequence Analysis Primer, Gribskov, M. and Devereux, J., eds., M Stockton Press, New York, 1991). In a preferred embodiment, the percent identity between two amino acid sequences is determined using the Needleman and Wunsch algorithm (J. Mol. Biol. (48):444-453 (1970)) which has been incorporated into the GAP program in the GCG software package, using either a Blossom 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6.

In yet another preferred embodiment, the percent identity between two nucleotide sequences is determined using the GAP program in the GCG software package (Devereux, J., et al., Nucleic Acids Res. 12(1):387 (1984)), using a NWSgapdna.CMP matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. In another embodiment, the percent identity between two amino acid or nucleotide sequences is determined using the algorithm of E. Myers and W. Miller (CABIOS, 4:11-17 (1989)) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12, and a gap penalty of 4.

The nucleotide and amino acid sequences of the present invention can further be used as a “query sequence” to perform a search against sequence databases to, for example, identify other family members or related sequences. Such searches can be performed using the NBLAST and XBLAST programs (version 2.0) of Altschul, et al. (J. Mol. Biol. 215:403-10 (1990)). BLAST nucleotide searches can be performed with the NBLAST program, score=100, wordlength=12 to obtain nucleotide sequences homologous to the nucleic acid molecules of the invention. BLAST protein searches can be performed with the XBLAST program, score=50, wordlength=3 to obtain amino acid sequences homologous to the proteins of the invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al. (Nucleic Acids Res. 25(17):3389-3402 (1997)). When utilizing BLAST and gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used. In addition to BLAST, examples of other search and sequence comparison programs used in the art include, but are not limited to, FASTA (Pearson, Methods Mol. Biol. 25, 365-389 (1994)) and KERR (Dufresne et al., Nat Biotechnol 2002 December; 20(12): 1269-71). For further information regarding bioinformatics techniques, see Current Protocols in Bioinformatics, John Wiley & Sons, Inc., N.Y.

The present invention further provides non-coding fragments of the nucleic acid molecules disclosed in Table 1 and/or Table 2. Preferred non-coding fragments include, but are not limited to, promoter sequences, enhancer sequences, intronic sequences, 5′ untranslated regions (UTRs), 3′ untranslated regions, gene modulating sequences and gene termination sequences. Such fragments are useful, for example, in controlling heterologous gene expression and in developing screens to identify gene-modulating agents.

SNP Detection Reagents

In a specific aspect of the present invention, the SNPs disclosed in Table 1 and/or Table 2, and their associated transcript sequences (provided in Table 1 as SEQ ID NOS:1-2), genomic sequences (provided in Table 2 as SEQ ID NOS:10-18), and context sequences (transcript-based context sequences are provided in Table 1 as SEQ ID NOS:5-9; genomic-based context sequences are provided in Table 2 as SEQ ID NOS:19-110), can be used for the design of SNP detection reagents. As used herein, a “SNP detection reagent” is a reagent that specifically detects a specific target SNP position disclosed herein, and that is preferably specific for a particular nucleotide (allele) of the target SNP position (i.e., the detection reagent preferably can differentiate between different alternative nucleotides at a target SNP position, thereby allowing the identity of the nucleotide present at the target SNP position to be determined). Typically, such detection reagent hybridizes to a target SNP-containing nucleic acid molecule by complementary base-pairing in a sequence specific manner, and discriminates the target variant sequence from other nucleic acid sequences such as an art-known form in a test sample. An example of a detection reagent is a probe that hybridizes to a target nucleic acid containing one or more of the SNPs provided in Table 1 and/or Table 2. In a preferred embodiment, such a probe can differentiate between nucleic acids having a particular nucleotide (allele) at a target SNP position from other nucleic acids that have a different nucleotide at the same target SNP position. In addition, a detection reagent may hybridize to a specific region 5′ and/or 3′ to a SNP position, particularly a region corresponding to the context sequences provided in Table 1 and/or Table 2 (transcript-based context sequences are provided in Table 1 as SEQ ID NOS:5-9; genomic-based context sequences are provided in Table 2 as SEQ ID NOS: 19-110). Another example of a detection reagent is a primer which acts as an initiation point of nucleotide extension along a complementary strand of a target polynucleotide. The SNP sequence information provided herein is also useful for designing primers, e.g. allele-specific primers, to amplify (e.g., using PCR) any SNP of the present invention.

In one preferred embodiment of the invention, a SNP detection reagent is an isolated or synthetic DNA or RNA polynucleotide probe or primer or PNA oligomer, or a combination of DNA, RNA and/or PNA, that hybridizes to a segment of a target nucleic acid molecule containing a SNP identified in Table 1 and/or Table 2. A detection reagent in the form of a polynucleotide may optionally contain modified base analogs, intercalators or minor groove binders. Multiple detection reagents such as probes may be, for example, affixed to a solid support (e.g., arrays or beads) or supplied in solution (e.g., probe/primer sets for enzymatic reactions such as PCR, RT-PCR, TaqMan assays, or primer-extension reactions) to form a SNP detection kit.

A probe or primer typically is a substantially purified oligonucleotide or PNA oligomer. Such oligonucleotide typically comprises a region of complementary nucleotide sequence that hybridizes under stringent conditions to at least about 8, 10, 12, 16, 18, 20, 22, 25, 30, 40, 50, 55, 60, 65, 70, 80, 90, 100, 120 (or any other number in-between) or more consecutive nucleotides in a target nucleic acid molecule. Depending on the particular assay, the consecutive nucleotides can either include the target SNP position, or be a specific region in close enough proximity 5′ and/or 3′ to the SNP position to carry out the desired assay.

Other preferred primer and probe sequences can readily be determined using the transcript sequences (SEQ ID NOS:1-2), genomic sequences (SEQ ID NOS:10-18), and SNP context sequences (transcript-based context sequences are provided in Table 1 as SEQ ID NOS:5-9; genomic-based context sequences are provided in Table 2 as SEQ ID NOS:19-110) disclosed in the Sequence Listing and in Tables 1-2. It will be apparent to one of skill in the art that such primers and probes are directly useful as reagents for genotyping the SNPs of the present invention, and can be incorporated into any kit/system format.

In order to produce a probe or primer specific for a target SNP-containing sequence, the gene/transcript and/or context sequence surrounding the SNP of interest is typically examined using a computer algorithm which starts at the 5′ or at the 3′ end of the nucleotide sequence. Typical algorithms will then identify oligomers of defined length that are unique to the gene/SNP context sequence, have a GC content within a range suitable for hybridization, lack predicted secondary structure that may interfere with hybridization, and/or possess other desired characteristics or that lack other undesired characteristics.

A primer or probe of the present invention is typically at least about 8 nucleotides in length. In one embodiment of the invention, a primer or a probe is at least about 10 nucleotides in length. In a preferred embodiment, a primer or a probe is at least about 12 nucleotides in length. In a more preferred embodiment, a primer or probe is at least about 16, 17, 18, 19, 20, 21, 22, 23, 24 or 25 nucleotides in length. While the maximal length of a probe can be as long as the target sequence to be detected, depending on the type of assay in which it is employed, it is typically less than about 50, 60, 65, or 70 nucleotides in length. In the case of a primer, it is typically less than about 30 nucleotides in length. In a specific preferred embodiment of the invention, a primer or a probe is within the length of about 18 and about 28 nucleotides. However, in other embodiments, such as nucleic acid arrays and other embodiments in which probes are affixed to a substrate, the probes can be longer, such as on the order of 30-70, 75, 80, 90, 100, or more nucleotides in length (see the section below entitled “SNP Detection Kits and Systems”).

For analyzing SNPs, it may be appropriate to use oligonucleotides specific for alternative SNP alleles. Such oligonucleotides which detect single nucleotide variations in target sequences may be referred to by such terms as “allele-specific oligonucleotides”, “allele-specific probes”, or “allele-specific primers”. The design and use of allele-specific probes for analyzing polymorphisms is described in, e.g., Mutation Detection A Practical Approach, ed. Cotton et al. Oxford University Press, 1998; Saiki et al., Nature 324, 163-166 (1986); Dattagupta, EP235,726; and Saiki, WO 89/11548.

While the design of each allele-specific primer or probe depends on variables such as the precise composition of the nucleotide sequences flanking a SNP position in a target nucleic acid molecule, and the length of the primer or probe, another factor in the use of primers and probes is the stringency of the condition under which the hybridization between the probe or primer and the target sequence is performed. Higher stringency conditions utilize buffers with lower ionic strength and/or a higher reaction temperature, and tend to require a more perfect match between probe/primer and a target sequence in order to form a stable duplex. If the stringency is too high, however, hybridization may not occur at all. In contrast, lower stringency conditions utilize buffers with higher ionic strength and/or a lower reaction temperature, and permit the formation of stable duplexes with more mismatched bases between a probe/primer and a target sequence. By way of example and not limitation, exemplary conditions for high stringency hybridization conditions using an allele-specific probe are as follows: Prehybridization with a solution containing 5× standard saline phosphate EDTA (SSPE), 0.5% NaDodSO₄ (SDS) at 55° C., and incubating probe with target nucleic acid molecules in the same solution at the same temperature, followed by washing with a solution containing 2×SSPE, and 0.1% SDS at 55° C. or room temperature.

Moderate stringency hybridization conditions may be used for allele-specific primer extension reactions with a solution containing, e.g., about 50 mM KCl at about 46° C. Alternatively, the reaction may be carried out at an elevated temperature such as 60° C. In another embodiment, a moderately stringent hybridization condition suitable for oligonucleotide ligation assay (OLA) reactions wherein two probes are ligated if they are completely complementary to the target sequence may utilize a solution of about 100 mM KCl at a temperature of 46° C.

In a hybridization-based assay, allele-specific probes can be designed that hybridize to a segment of target DNA from one individual but do not hybridize to the corresponding segment from another individual due to the presence of different polymorphic forms (e.g., alternative SNP alleles/nucleotides) in the respective DNA segments from the two individuals. Hybridization conditions should be sufficiently stringent that there is a significant detectable difference in hybridization intensity between alleles, and preferably an essentially binary response, whereby a probe hybridizes to only one of the alleles or significantly more strongly to one allele. While a probe may be designed to hybridize to a target sequence that contains a SNP site such that the SNP site aligns anywhere along the sequence of the probe, the probe is preferably designed to hybridize to a segment of the target sequence such that the SNP site aligns with a central position of the probe (e.g., a position within the probe that is at least three nucleotides from either end of the probe). This design of probe generally achieves good discrimination in hybridization between different allelic forms.

In another embodiment, a probe or primer may be designed to hybridize to a segment of target DNA such that the SNP aligns with either the 5′ most end or the 3′ most end of the probe or primer. In a specific preferred embodiment which is particularly suitable for use in a oligonucleotide ligation assay (U.S. Pat. No. 4,988,617), the 3′ most nucleotide of the probe aligns with the SNP position in the target sequence.

Oligonucleotide probes and primers may be prepared by methods well known in the art. Chemical synthetic methods include, but are limited to, the phosphotriester method described by Narang et al., 1979, Methods in Enzymology 68:90; the phosphodiester method described by Brown et al., 1979, Methods in Enzymology 68:109, the diethylphosphoamidate method described by Beaucage et al., 1981, Tetrahedron Letters 22:1859; and the solid support method described in U.S. Pat. No. 4,458,066.

Allele-specific probes are often used in pairs (or, less commonly, in sets of 3 or 4, such as if a SNP position is known to have 3 or 4 alleles, respectively, or to assay both strands of a nucleic acid molecule for a target SNP allele), and such pairs may be identical except for a one nucleotide mismatch that represents the allelic variants at the SNP position. Commonly, one member of a pair perfectly matches a reference form of a target sequence that has a more common SNP allele (i.e., the allele that is more frequent in the target population) and the other member of the pair perfectly matches a form of the target sequence that has a less common SNP allele (i.e., the allele that is rarer in the target population). In the case of an array, multiple pairs of probes can be immobilized on the same support for simultaneous analysis of multiple different polymorphisms.

In one type of PCR-based assay, an allele-specific primer hybridizes to a region on a target nucleic acid molecule that overlaps a SNP position and only primes amplification of an allelic form to which the primer exhibits perfect complementarity (Gibbs, 1989, Nucleic Acid Res. 17 2427-2448). Typically, the primer's 3′-most nucleotide is aligned with and complementary to the SNP position of the target nucleic acid molecule. This primer is used in conjunction with a second primer that hybridizes at a distal site. Amplification proceeds from the two primers, producing a detectable product that indicates which allelic form is present in the test sample. A control is usually performed with a second pair of primers, one of which shows a single base mismatch at the polymorphic site and the other of which exhibits perfect complementarity to a distal site. The single-base mismatch prevents amplification or substantially reduces amplification efficiency, so that either no detectable product is formed or it is formed in lower amounts or at a slower pace. The method generally works most effectively when the mismatch is at the 3′-most position of the oligonucleotide (i.e., the 3′-most position of the oligonucleotide aligns with the target SNP position) because this position is most destabilizing to elongation from the primer (see, e.g., WO 93/22456). This PCR-based assay can be utilized as part of the TaqMan assay, described below.

In a specific embodiment of the invention, a primer of the invention contains a sequence substantially complementary to a segment of a target SNP-containing nucleic acid molecule except that the primer has a mismatched nucleotide in one of the three nucleotide positions at the 3′-most end of the primer, such that the mismatched nucleotide does not base pair with a particular allele at the SNP site. In a preferred embodiment, the mismatched nucleotide in the primer is the second from the last nucleotide at the 3′-most position of the primer. In a more preferred embodiment, the mismatched nucleotide in the primer is the last nucleotide at the 3′-most position of the primer.

In another embodiment of the invention, a SNP detection reagent of the invention is labeled with a fluorogenic reporter dye that emits a detectable signal. While the preferred reporter dye is a fluorescent dye, any reporter dye that can be attached to a detection reagent such as an oligonucleotide probe or primer is suitable for use in the invention. Such dyes include, but are not limited to, Acridine, AMCA, BODIPY, Cascade Blue, Cy2, Cy3, Cy5, Cy7, Dabcyl, Edans, Eosin, Erythrosin, Fluorescein, 6-Fam, Tet, Joe, Hex, Oregon Green, Rhodamine, Rhodol Green, Tamra, Rox, and Texas Red.

In yet another embodiment of the invention, the detection reagent may be further labeled with a quencher dye such as Tamra, especially when the reagent is used as a self-quenching probe such as a TaqMan (U.S. Pat. Nos. 5,210,015 and 5,538,848) or Molecular Beacon probe (U.S. Pat. Nos. 5,118,801 and 5,312,728), or other stemless or linear beacon probe (Livak et al., 1995, PCR Method Appl. 4:357-362; Tyagi et al., 1996, Nature Biotechnology 14: 303-308; Nazarenko et al., 1997, Nucl. Acids Res. 25:2516-2521; U.S. Pat. Nos. 5,866,336 and 6,117,635).

The detection reagents of the invention may also contain other labels, including but not limited to, biotin for streptavidin binding, hapten for antibody binding, and oligonucleotide for binding to another complementary oligonucleotide such as pairs of zipcodes.

The present invention also contemplates reagents that do not contain (or that are complementary to) a SNP nucleotide identified herein but that are used to assay one or more SNPs disclosed herein. For example, primers that flank, but do not hybridize directly to a target SNP position provided herein are useful in primer extension reactions in which the primers hybridize to a region adjacent to the target SNP position (i.e., within one or more nucleotides from the target SNP site). During the primer extension reaction, a primer is typically not able to extend past a target SNP site if a particular nucleotide (allele) is present at that target SNP site, and the primer extension product can be detected in order to determine which SNP allele is present at the target SNP site. For example, particular ddNTPs are typically used in the primer extension reaction to terminate primer extension once a ddNTP is incorporated into the extension product (a primer extension product which includes a ddNTP at the 3′-most end of the primer extension product, and in which the ddNTP is a nucleotide of a SNP disclosed herein, is a composition that is specifically contemplated by the present invention). Thus, reagents that bind to a nucleic acid molecule in a region adjacent to a SNP site and that are used for assaying the SNP site, even though the bound sequences do not necessarily include the SNP site itself, are also contemplated by the present invention.

SNP Detection Kits and Systems

A person skilled in the art will recognize that, based on the SNP and associated sequence information disclosed herein, detection reagents can be developed and used to assay any SNP of the present invention individually or in combination, and such detection reagents can be readily incorporated into one of the established kit or system formats which are well known in the art. The terms “kits” and “systems”, as used herein in the context of SNP detection reagents, are intended to refer to such things as combinations of multiple SNP detection reagents, or one or more SNP detection reagents in combination with one or more other types of elements or components (e.g., other types of biochemical reagents, containers, packages such as packaging intended for commercial sale, substrates to which SNP detection reagents are attached, electronic hardware components, etc.). Accordingly, the present invention further provides SNP detection kits and systems, including but not limited to, packaged probe and primer sets (e.g., TaqMan probe/primer sets), arrays/microarrays of nucleic acid molecules, and beads that contain one or more probes, primers, or other detection reagents for detecting one or more SNPs of the present invention. The kits/systems can optionally include various electronic hardware components; for example, arrays (“DNA chips”) and microfluidic systems (“lab-on-a-chip” systems) provided by various manufacturers typically comprise hardware components. Other kits/systems (e.g., probe/primer sets) may not include electronic hardware components, but may be comprised of, for example, one or more SNP detection reagents (along with, optionally, other biochemical reagents) packaged in one or more containers.

In some embodiments, a SNP detection kit typically contains one or more detection reagents and other components (e.g., a buffer, enzymes such as DNA polymerases or ligases, chain extension nucleotides such as deoxynucleotide triphosphates, and in the case of Sanger-type DNA sequencing reactions, chain terminating nucleotides, positive control sequences, negative control sequences, and the like) necessary to carry out an assay or reaction, such as amplification and/or detection of a SNP-containing nucleic acid molecule. A kit may further contain means for determining the amount of a target nucleic acid, and means for comparing the amount with a standard, and can comprise instructions for using the kit to detect the SNP-containing nucleic acid molecule of interest. In one embodiment of the present invention, kits are provided which contain the necessary reagents to carry out one or more assays to detect one or more SNPs disclosed herein. In a preferred embodiment of the present invention, SNP detection kits/systems are in the form of nucleic acid arrays, or compartmentalized kits, including microfluidic/lab-on-a-chip systems.

SNP detection kits/systems may contain, for example, one or more probes, or pairs of probes, that hybridize to a nucleic acid molecule at or near each target SNP position. Multiple pairs of allele-specific probes may be included in the kit/system to simultaneously assay large numbers of SNPs, at least one of which is a SNP of the present invention. In some kits/systems, the allele-specific probes are immobilized to a substrate such as an array or bead. For example, the same substrate can comprise allele-specific probes for detecting at least 1; 10; 100; 1000; 10,000; 100,000 (or any other number in-between) or substantially all of the SNPs shown in Table 1 and/or Table 2.

The terms “arrays”, “microarrays”, and “DNA chips” are used herein interchangeably to refer to an array of distinct polynucleotides affixed to a substrate, such as glass, plastic, paper, nylon or other type of membrane, filter, chip, or any other suitable solid support. The polynucleotides can be synthesized directly on the substrate, or synthesized separate from the substrate and then affixed to the substrate. In one embodiment, the microarray is prepared and used according to the methods described in U.S. Pat. No. 5,837,832, Chee et al., PCT application WO95/11995 (Chee et al.), Lockhart, D. J. et al. (1996; Nat. Biotech. 14: 1675-1680) and Schena, M. et al. (1996; Proc. Natl. Acad. Sci. 93: 10614-10619), all of which are incorporated herein in their entirety by reference. In other embodiments, such arrays are produced by the methods described by Brown et al., U.S. Pat. No. 5,807,522.

Nucleic acid arrays are reviewed in the following references: Zammatteo et al., “New chips for molecular biology and diagnostics”, Biotechnol Annu Rev. 2002; 8:85-101; Sosnowski et al., “Active microelectronic array system for DNA hybridization, genotyping and pharmacogenomic applications”, Psychiatr Genet. 2002 December; 12(4):181-92; Heller, “DNA microarray technology: devices, systems, and applications”, Annu Rev Biomed Eng. 2002; 4:129-53. Epub 2002 Mar. 22; Kolchinsky et al., “Analysis of SNPs and other genomic variations using gel-based chips”, Hum Mutat. 2002 April; 19(4):343-60; and McGall et al., “High-density genechip oligonucleotide probe arrays”, Adv Biochem Eng Biotechnol. 2002; 77:21-42.

Any number of probes, such as allele-specific probes, may be implemented in an array, and each probe or pair of probes can hybridize to a different SNP position. In the case of polynucleotide probes, they can be synthesized at designated areas (or synthesized separately and then affixed to designated areas) on a substrate using a light-directed chemical process. Each DNA chip can contain, for example, thousands to millions of individual synthetic polynucleotide probes arranged in a grid-like pattern and miniaturized (e.g., to the size of a dime). Preferably, probes are attached to a solid support in an ordered, addressable array.

A microarray can be composed of a large number of unique, single-stranded polynucleotides, usually either synthetic antisense polynucleotides or fragments of cDNAs, fixed to a solid support. Typical polynucleotides are preferably about 6-60 nucleotides in length, more preferably about 15-30 nucleotides in length, and most preferably about 18-25 nucleotides in length. For certain types of microarrays or other detection kits/systems, it may be preferable to use oligonucleotides that are only about 7-20 nucleotides in length. In other types of arrays, such as arrays used in conjunction with chemiluminescent detection technology, preferred probe lengths can be, for example, about 15-80 nucleotides in length, preferably about 50-70 nucleotides in length, more preferably about 55-65 nucleotides in length, and most preferably about 60 nucleotides in length. The microarray or detection kit can contain polynucleotides that cover the known 5′ or 3′ sequence of a gene/transcript or target SNP site, sequential polynucleotides that cover the full-length sequence of a gene/transcript; or unique polynucleotides selected from particular areas along the length of a target gene/transcript sequence, particularly areas corresponding to one or more SNPs disclosed in Table 1 and/or Table 2. Polynucleotides used in the microarray or detection kit can be specific to a SNP or SNPs of interest (e.g., specific to a particular SNP allele at a target SNP site, or specific to particular SNP alleles at multiple different SNP sites), or specific to a polymorphic gene/transcript or genes/transcripts of interest.

Hybridization assays based on polynucleotide arrays rely on the differences in hybridization stability of the probes to perfectly matched and mismatched target sequence variants. For SNP genotyping, it is generally preferable that stringency conditions used in hybridization assays are high enough such that nucleic acid molecules that differ from one another at as little as a single SNP position can be differentiated (e.g., typical SNP hybridization assays are designed so that hybridization will occur only if one particular nucleotide is present at a SNP position, but will not occur if an alternative nucleotide is present at that SNP position). Such high stringency conditions may be preferable when using, for example, nucleic acid arrays of allele-specific probes for SNP detection. Such high stringency conditions are described in the preceding section, and are well known to those skilled in the art and can be found in, for example, Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.1-6.3.6.

In other embodiments, the arrays are used in conjunction with chemiluminescent detection technology. The following patents and patent applications, which are all hereby incorporated by reference, provide additional information pertaining to chemiluminescent detection: U.S. patent application Ser. Nos. 10/620,332 and 10/620,333 describe chemiluminescent approaches for microarray detection; U.S. Pat. Nos. 6,124,478, 6,107,024, 5,994,073, 5,981,768, 5,871,938, 5,843,681, 5,800,999, and 5,773,628 describe methods and compositions of dioxetane for performing chemiluminescent detection; and U.S. published application US2002/0110828 discloses methods and compositions for microarray controls.

In one embodiment of the invention, a nucleic acid array can comprise an array of probes of about 15-25 nucleotides in length. In further embodiments, a nucleic acid array can comprise any number of probes, in which at least one probe is capable of detecting one or more SNPs disclosed in Table 1 and/or Table 2, and/or at least one probe comprises a fragment of one of the sequences selected from the group consisting of those disclosed in Table 1, Table 2, the Sequence Listing, and sequences complementary thereto, said fragment comprising at least about 8 consecutive nucleotides, preferably 10, 12, 15, 16, 18, 20, more preferably 22, 25, 30, 40, 47, 50, 55, 60, 65, 70, 80, 90, 100, or more consecutive nucleotides (or any other number in-between) and containing (or being complementary to) a novel SNP allele disclosed in Table 1 and/or Table 2. In some embodiments, the nucleotide complementary to the SNP site is within 5, 4, 3, 2, or I nucleotide from the center of the probe, more preferably at the center of said probe.

A polynucleotide probe can be synthesized on the surface of the substrate by using a chemical coupling procedure and an ink jet application apparatus, as described in PCT application WO95/251116 (Baldeschweiler et al.) which is incorporated herein in its entirety by reference. In another aspect, a “gridded” array analogous to a dot (or slot) blot may be used to arrange and link cDNA fragments or oligonucleotides to the surface of a substrate using a vacuum system, thermal, UV, mechanical or chemical bonding procedures. An array, such as those described above, may be produced by hand or by using available devices (slot blot or dot blot apparatus), materials (any suitable solid support), and machines (including robotic instruments), and may contain 8, 24, 96, 384, 1536, 6144 or more polynucleotides, or any other number which lends itself to the efficient use of commercially available instrumentation.

Using such arrays or other kits/systems, the present invention provides methods of identifying the SNPs disclosed herein in a test sample. Such methods typically involve incubating a test sample of nucleic acids with an array comprising one or more probes corresponding to at least one SNP position of the present invention, and assaying for binding of a nucleic acid from the test sample with one or more of the probes. Conditions for incubating a SNP detection reagent (or a kit/system that employs one or more such SNP detection reagents) with a test sample vary. Incubation conditions depend on such factors as the format employed in the assay, the detection methods employed, and the type and nature of the detection reagents used in the assay. One skilled in the art will recognize that any one of the commonly available hybridization, amplification and array assay formats can readily be adapted to detect the SNPs disclosed herein.

A SNP detection kit/system of the present invention may include components that are used to prepare nucleic acids from a test sample for the subsequent amplification and/or detection of a SNP-containing nucleic acid molecule. Such sample preparation components can be used to produce nucleic acid extracts (including DNA and/or RNA), proteins or membrane extracts from any bodily fluids (such as blood, serum, plasma, urine, saliva, phlegm, gastric juices, semen, tears, sweat, etc.), skin, hair, cells (especially nucleated cells), biopsies, buccal swabs or tissue specimens. The test samples used in the above-described methods will vary based on such factors as the assay format, nature of the detection method, and the specific tissues, cells or extracts used as the test sample to be assayed. Methods of preparing nucleic acids, proteins, and cell extracts are well known in the art and can be readily adapted to obtain a sample that is compatible with the system utilized. Automated sample preparation systems for extracting nucleic acids from a test sample are commercially available, and examples are Qiagen's BioRobot 9600, Applied Biosystems' PRISM™ 6700 sample preparation system, and Roche Molecular Systems'COBAS AmpliPrep System.

Another form of kit contemplated by the present invention is a compartmentalized kit. A compartmentalized kit includes any kit in which reagents are contained in separate containers. Such containers include, for example, small glass containers, plastic containers, strips of plastic, glass or paper, or arraying material such as silica. Such containers allow one to efficiently transfer reagents from one compartment to another compartment such that the test samples and reagents are not cross-contaminated, or from one container to another vessel not included in the kit, and the agents or solutions of each container can be added in a quantitative fashion from one compartment to another or to another vessel. Such containers may include, for example, one or more containers which will accept the test sample, one or more containers which contain at least one probe or other SNP detection reagent for detecting one or more SNPs of the present invention, one or more containers which contain wash reagents (such as phosphate buffered saline, Tris-buffers, etc.), and one or more containers which contain the reagents used to reveal the presence of the bound probe or other SNP detection reagents. The kit can optionally further comprise compartments and/or reagents for, for example, nucleic acid amplification or other enzymatic reactions such as primer extension reactions, hybridization, ligation, electrophoresis (preferably capillary electrophoresis), mass spectrometry, and/or laser-induced fluorescent detection. The kit may also include instructions for using the kit. Exemplary compartmentalized kits include microfluidic devices known in the art (see, e.g., Weigl et al., “Lab-on-a-chip for drug development”, Adv Drug Deliv Rev. 2003 Feb. 24; 55(3):349-77). In such microfluidic devices, the containers may be referred to as, for example, microfluidic “compartments”, “chambers”, or “channels”.

Microfluidic devices, which may also be referred to as “lab-on-a-chip” systems, biomedical micro-electro-mechanical systems (bioMEMs), or multicomponent integrated systems, are exemplary kits/systems of the present invention for analyzing SNPs. Such systems miniaturize and compartmentalize processes such as probe/target hybridization, nucleic acid amplification, and capillary electrophoresis reactions in a single functional device. Such microfluidic devices typically utilize detection reagents in at least one aspect of the system, and such detection reagents may be used to detect one or more SNPs of the present invention. One example of a microfluidic system is disclosed in U.S. Pat. No. 5,589,136, which describes the integration of PCR amplification and capillary electrophoresis in chips. Exemplary microfluidic systems comprise a pattern of microchannels designed onto a glass, silicon, quartz, or plastic wafer included on a microchip. The movements of the samples may be controlled by electric, electroosmotic or hydrostatic forces applied across different areas of the microchip to create functional microscopic valves and pumps with no moving parts. Varying the voltage can be used as a means to control the liquid flow at intersections between the micro-machined channels and to change the liquid flow rate for pumping across different sections of the microchip. See, for example, U.S. Pat. Nos. 6,153,073, Dubrow et al., and 6,156,181, Parce et al.

For genotyping SNPs, an exemplary microfluidic system may integrate, for example, nucleic acid amplification, primer extension, capillary electrophoresis, and a detection method such as laser induced fluorescence detection. In a first step of an exemplary process for using such an exemplary system, nucleic acid samples are amplified, preferably by PCR. Then, the amplification products are subjected to automated primer extension reactions using ddNTPs (specific fluorescence for each ddNTP) and the appropriate oligonucleotide primers to carry out primer extension reactions which hybridize just upstream of the targeted SNP. Once the extension at the 3′ end is completed, the primers are separated from the unincorporated fluorescent ddNTPs by capillary electrophoresis. The separation medium used in capillary electrophoresis can be, for example, polyacrylamide, polyethyleneglycol or dextran. The incorporated ddNTPs in the single nucleotide primer extension products are identified by laser-induced fluorescence detection. Such an exemplary microchip can be used to process, for example, at least 96 to 384 samples, or more, in parallel.

Uses of Nucleic Acid Molecules

The nucleic acid molecules of the present invention have a variety of uses, especially in the diagnosis and treatment of psoriasis and related pathologies. For example, the nucleic acid molecules are useful as hybridization probes, such as for genotyping SNPs in messenger RNA, transcript, cDNA, genomic DNA, amplified DNA or other nucleic acid molecules, and for isolating full-length cDNA and genomic clones encoding the variant peptides disclosed in Table 1 as well as their orthologs.

A probe can hybridize to any nucleotide sequence along the entire length of a nucleic acid molecule provided in Table 1 and/or Table 2. Preferably, a probe of the present invention hybridizes to a region of a target sequence that encompasses a SNP position indicated in Table 1 and/or Table 2. More preferably, a probe hybridizes to a SNP-containing target sequence in a sequence-specific manner such that it distinguishes the target sequence from other nucleotide sequences which vary from the target sequence only by which nucleotide is present at the SNP site. Such a probe is particularly useful for detecting the presence of a SNP-containing nucleic acid in a test sample, or for determining which nucleotide (allele) is present at a particular SNP site (i.e., genotyping the SNP site).

A nucleic acid hybridization probe may be used for determining the presence, level, form, and/or distribution of nucleic acid expression. The nucleic acid whose level is determined can be DNA or RNA. Accordingly, probes specific for the SNPs described herein can be used to assess the presence, expression and/or gene copy number in a given cell, tissue, or organism. These uses are relevant for diagnosis of disorders involving an increase or decrease in gene expression relative to normal levels. In vitro techniques for detection of mRNA include, for example, Northern blot hybridizations and in situ hybridizations. In vitro techniques for detecting DNA include Southern blot hybridizations and in situ hybridizations (Sambrook and Russell, 2000, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Press, Cold Spring Harbor, N.Y.).

Probes can be used as part of a diagnostic test kit for identifying cells or tissues in which a variant protein is expressed, such as by measuring the level of a variant protein-encoding nucleic acid (e.g., mRNA) in a sample of cells from a subject or determining if a polynucleotide contains a SNP of interest.

Thus, the nucleic acid molecules of the invention can be used as hybridization probes to detect the SNPs disclosed herein, thereby determining whether an individual with the polymorphisms is at risk for psoriasis and related pathologies or has developed early stage psoriasis. Detection of a SNP associated with a disease phenotype provides a diagnostic tool for an active disease and/or genetic predisposition to the disease.

Furthermore, the nucleic acid molecules of the invention are therefore useful for detecting a gene (gene information is disclosed in Table 2, for example) which contains a SNP disclosed herein and/or products of such genes, such as expressed mRNA transcript molecules (transcript information is disclosed in Table 1, for example), and are thus useful for detecting gene expression. The nucleic acid molecules can optionally be implemented in, for example, an array or kit format for use in detecting gene expression.

The nucleic acid molecules of the invention are also useful as primers to amplify any given region of a nucleic acid molecule, particularly a region containing a SNP identified in Table 1 and/or Table 2.

The nucleic acid molecules of the invention are also useful for constructing recombinant vectors (described in greater detail below). Such vectors include expression vectors that express a portion of, or all of, any of the variant peptide sequences provided in Table 1. Vectors also include insertion vectors, used to integrate into another nucleic acid molecule sequence, such as into the cellular genome, to alter in situ expression of a gene and/or gene product. For example, an endogenous coding sequence can be replaced via homologous recombination with all or part of the coding region containing one or more specifically introduced SNPs.

The nucleic acid molecules of the invention are also useful for expressing antigenic portions of the variant proteins, particularly antigenic portions that contain a variant amino acid sequence (e.g., an amino acid substitution) caused by a SNP disclosed in Table 1 and/or Table 2.

The nucleic acid molecules of the invention are also useful for constructing vectors containing a gene regulatory region of the nucleic acid molecules of the present invention.

The nucleic acid molecules of the invention are also useful for designing ribozymes corresponding to all, or a part, of an mRNA molecule expressed from a SNP-containing nucleic acid molecule described herein.

The nucleic acid molecules of the invention are also useful for constructing host cells expressing a part, or all, of the nucleic acid molecules and variant peptides.

The nucleic acid molecules of the invention are also useful for constructing transgenic animals expressing all, or a part, of the nucleic acid molecules and variant peptides. The production of recombinant cells and transgenic animals having nucleic acid molecules which contain the SNPs disclosed in Table 1 and/or Table 2 allow, for example, effective clinical design of treatment compounds and dosage regimens.

The nucleic acid molecules of the invention are also useful in assays for drug screening to identify compounds that, for example, modulate nucleic acid expression.

The nucleic acid molecules of the invention are also useful in gene therapy in patients whose cells have aberrant gene expression. Thus, recombinant cells, which include a patient's cells that have been engineered ex vivo and returned to the patient, can be introduced into an individual where the recombinant cells produce the desired protein to treat the individual.

SNP Genotyping Methods

The process of determining which specific nucleotide (i.e., allele) is present at each of one or more SNP positions, such as a SNP position in a nucleic acid molecule disclosed in Table 1 and/or Table 2, is referred to as SNP genotyping. The present invention provides methods of SNP genotyping, such as for use in screening for psoriasis or related pathologies, or determining predisposition thereto, or determining responsiveness to a form of treatment, or in genome mapping or SNP association analysis, etc.

Nucleic acid samples can be genotyped to determine which allele(s) is/are present at any given genetic region (e.g., SNP position) of interest by methods well known in the art. The neighboring sequence can be used to design SNP detection reagents such as oligonucleotide probes, which may optionally be implemented in a kit format. Exemplary SNP genotyping methods are described in Chen et al., “Single nucleotide polymorphism genotyping: biochemistry, protocol, cost and throughput”, Pharmacogenomics J. 2003; 3(2):77-96; Kwok et al., “Detection of single nucleotide polymorphisms”, Curr Issues Mol. Biol. 2003 April; 5(2):43-60; Shi, “Technologies for individual genotyping: detection of genetic polymorphisms in drug targets and disease genes”, Am J Pharmacogenomics. 2002; 2(3):197-205; and Kwok, “Methods for genotyping single nucleotide polymorphisms”, Annu Rev Genomics Hum Genet 2001; 2:235-58. Exemplary techniques for high-throughput SNP genotyping are described in Marnellos, “High-throughput SNP analysis for genetic association studies”, Curr Opin Drug Discov Devel. 2003 May; 6(3):317-21. Common SNP genotyping methods include, but are not limited to, TaqMan assays, molecular beacon assays, nucleic acid arrays, allele-specific primer extension, allele-specific PCR, arrayed primer extension, homogeneous primer extension assays, primer extension with detection by mass spectrometry, pyrosequencing, multiplex primer extension sorted on genetic arrays, ligation with rolling circle amplification, homogeneous ligation, OLA (U.S. Pat. No. 4,988,167), multiplex ligation reaction sorted on genetic arrays, restriction-fragment length polymorphism, single base extension-tag assays, and the Invader assay. Such methods may be used in combination with detection mechanisms such as, for example, luminescence or chemiluminescence detection, fluorescence detection, time-resolved fluorescence detection, fluorescence resonance energy transfer, fluorescence polarization, mass spectrometry, and electrical detection.

Various methods for detecting polymorphisms include, but are not limited to, methods in which protection from cleavage agents is used to detect mismatched bases in RNA/RNA or RNA/DNA duplexes (Myers et al., Science 230:1242 (1985); Cotton et al., PNAS 85:4397 (1988); and Saleeba et al., Meth. Enzymol. 217:286-295 (1992)), comparison of the electrophoretic mobility of variant and wild type nucleic acid molecules (Orita et al., PNAS 86:2766 (1989); Cotton et al., Mutat. Res. 285:125-144 (1993); and Hayashi et al., Genet. Anal. Tech. Appl. 9:73-79 (1992)), and assaying the movement of polymorphic or wild-type fragments in polyacrylamide gels containing a gradient of denaturant using denaturing gradient gel electrophoresis (DGGE) (Myers et al., Nature 313:495 (1985)). Sequence variations at specific locations can also be assessed by nuclease protection assays such as RNase and S1 protection or chemical cleavage methods.

In a preferred embodiment, SNP genotyping is performed using the TaqMan assay, which is also known as the 5′ nuclease assay (U.S. Pat. Nos. 5,210,015 and 5,538,848). The TaqMan assay detects the accumulation of a specific amplified product during PCR. The TaqMan assay utilizes an oligonucleotide probe labeled with a fluorescent reporter dye and a quencher dye. The reporter dye is excited by irradiation at an appropriate wavelength, it transfers energy to the quencher dye in the same probe via a process called fluorescence resonance energy transfer (FRET). When attached to the probe, the excited reporter dye does not emit a signal. The proximity of the quencher dye to the reporter dye in the intact probe maintains a reduced fluorescence for the reporter. The reporter dye and quencher dye may be at the 5′ most and the 3′ most ends, respectively, or vice versa. Alternatively, the reporter dye may be at the 5′ or 3′ most end while the quencher dye is attached to an internal nucleotide, or vice versa. In yet another embodiment, both the reporter and the quencher may be attached to internal nucleotides at a distance from each other such that fluorescence of the reporter is reduced.

During PCR, the 5′ nuclease activity of DNA polymerase cleaves the probe, thereby separating the reporter dye and the quencher dye and resulting in increased fluorescence of the reporter. Accumulation of PCR product is detected directly by monitoring the increase in fluorescence of the reporter dye. The DNA polymerase cleaves the probe between the reporter dye and the quencher dye only if the probe hybridizes to the target SNP-containing template which is amplified during PCR, and the probe is designed to hybridize to the target SNP site only if a particular SNP allele is present.

Preferred TaqMan primer and probe sequences can readily be determined using the SNP and associated nucleic acid sequence information provided herein. A number of computer programs, such as Primer Express (Applied Biosystems, Foster City, Calif.), can be used to rapidly obtain optimal primer/probe sets. It will be apparent to one of skill in the art that such primers and probes for detecting the SNPs of the present invention are useful in diagnostic assays for psoriasis and related pathologies, and can be readily incorporated into a kit format. The present invention also includes modifications of the Taqman assay well known in the art such as the use of Molecular Beacon probes (U.S. Pat. Nos. 5,118,801 and 5,312,728) and other variant formats (U.S. Pat. Nos. 5,866,336 and 6,117,635).

Another preferred method for genotyping the SNPs of the present invention is the use of two oligonucleotide probes in an OLA (see, e.g., U.S. Pat. No. 4,988,617). In this method, one probe hybridizes to a segment of a target nucleic acid with its 3′ most end aligned with the SNP site. A second probe hybridizes to an adjacent segment of the target nucleic acid molecule directly 3′ to the first probe. The two juxtaposed probes hybridize to the target nucleic acid molecule, and are ligated in the presence of a linking agent such as a ligase if there is perfect complementarity between the 3′ most nucleotide of the first probe with the SNP site. If there is a mismatch, ligation would not occur. After the reaction, the ligated probes are separated from the target nucleic acid molecule, and detected as indicators of the presence of a SNP.

The following patents, patent applications, and published international patent applications, which are all hereby incorporated by reference, provide additional information pertaining to techniques for carrying out various types of OLA: U.S. Pat. Nos. 6,027,889, 6,268,148, 5,494,810, 5,830,711, and 6,054,564 describe OLA strategies for performing SNP detection; WO 97/31256 and WO 00/56927 describe OLA strategies for performing SNP detection using universal arrays, wherein a zipcode sequence can be introduced into one of the hybridization probes, and the resulting product, or amplified product, hybridized to a universal zip code array; U.S. application US01/17329 (and 09/584,905) describes OLA (or LDR) followed by PCR, wherein zipcodes are incorporated into OLA probes, and amplified PCR products are determined by electrophoretic or universal zipcode array readout; U.S. applications 60/427,818, 60/445,636, and 60/445,494 describe SNPlex methods and software for multiplexed SNP detection using OLA followed by PCR, wherein zipcodes are incorporated into OLA probes, and amplified PCR products are hybridized with a zipchute reagent, and the identity of the SNP determined from electrophoretic readout of the zipchute. In some embodiments, OLA is carried out prior to PCR (or another method of nucleic acid amplification). In other embodiments, PCR (or another method of nucleic acid amplification) is carried out prior to OLA.

Another method for SNP genotyping is based on mass spectrometry. Mass spectrometry takes advantage of the unique mass of each of the four nucleotides of DNA. SNPs can be unambiguously genotyped by mass spectrometry by measuring the differences in the mass of nucleic acids having alternative SNP alleles. MALDI-TOF (Matrix Assisted Laser Desorption Ionization—Time of Flight) mass spectrometry technology is preferred for extremely precise determinations of molecular mass, such as SNPs. Numerous approaches to SNP analysis have been developed based on mass spectrometry. Preferred mass spectrometry-based methods of SNP genotyping include primer extension assays, which can also be utilized in combination with other approaches, such as traditional gel-based formats and microarrays.

Typically, the primer extension assay involves designing and annealing a primer to a template PCR amplicon upstream (5′) from a target SNP position. A mix of dideoxynucleotide triphosphates (ddNTPs) and/or deoxynucleotide triphosphates (dNTPs) are added to a reaction mixture containing template (e.g., a SNP-containing nucleic acid molecule which has typically been amplified, such as by PCR), primer, and DNA polymerase. Extension of the primer terminates at the first position in the template where a nucleotide complementary to one of the ddNTPs in the mix occurs. The primer can be either immediately adjacent (i.e., the nucleotide at the 3′ end of the primer hybridizes to the nucleotide next to the target SNP site) or two or more nucleotides removed from the SNP position. If the primer is several nucleotides removed from the target SNP position, the only limitation is that the template sequence between the 3′ end of the primer and the SNP position cannot contain a nucleotide of the same type as the one to be detected, or this will cause premature termination of the extension primer. Alternatively, if all four ddNTPs alone, with no dNTPs, are added to the reaction mixture, the primer will always be extended by only one nucleotide, corresponding to the target SNP position. In this instance, primers are designed to bind one nucleotide upstream from the SNP position (i.e., the nucleotide at the 3′ end of the primer hybridizes to the nucleotide that is immediately adjacent to the target SNP site on the 5′ side of the target SNP site). Extension by only one nucleotide is preferable, as it minimizes the overall mass of the extended primer, thereby increasing the resolution of mass differences between alternative SNP nucleotides. Furthermore, mass-tagged ddNTPs can be employed in the primer extension reactions in place of unmodified ddNTPs. This increases the mass difference between primers extended with these ddNTPs, thereby providing increased sensitivity and accuracy, and is particularly useful for typing heterozygous base positions. Mass-tagging also alleviates the need for intensive sample-preparation procedures and decreases the necessary resolving power of the mass spectrometer.

The extended primers can then be purified and analyzed by MALDI-TOF mass spectrometry to determine the identity of the nucleotide present at the target SNP position. In one method of analysis, the products from the primer extension reaction are combined with light absorbing crystals that form a matrix. The matrix is then hit with an energy source such as a laser to ionize and desorb the nucleic acid molecules into the gas-phase. The ionized molecules are then ejected into a flight tube and accelerated down the tube towards a detector. The time between the ionization event, such as a laser pulse, and collision of the molecule with the detector is the time of flight of that molecule. The time of flight is precisely correlated with the mass-to-charge ratio (m/z) of the ionized molecule. Ions with smaller m/z travel down the tube faster than ions with larger m/z and therefore the lighter ions reach the detector before the heavier ions. The time-of-flight is then converted into a corresponding, and highly precise, m/z. In this manner, SNPs can be identified based on the slight differences in mass, and the corresponding time of flight differences, inherent in nucleic acid molecules having different nucleotides at a single base position. For further information regarding the use of primer extension assays in conjunction with MALDI-TOF mass spectrometry for SNP genotyping, see, e.g., Wise et al., “A standard protocol for single nucleotide primer extension in the human genome using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry”, Rapid Commun Mass Spectrom. 2003; 17(11):1195-202.

The following references provide further information describing mass spectrometry-based methods for SNP genotyping: Bocker, “SNP and mutation discovery using base-specific cleavage and MALDI-TOF mass spectrometry”, Bioinformatics. 2003 July; 19 Suppl 1:144-153; Storm et al., “MALDI-TOF mass spectrometry-based SNP genotyping”, Methods Mol. Biol. 2003; 212:241-62; Jurinke et al., “The use of Mass ARRAY technology for high throughput genotyping”, Adv Biochem Eng Biotechnol. 2002; 77:57-74; and Jurinke et al., “Automated genotyping using the DNA MassArray technology”, Methods Mol. Biol. 2002; 187:179-92.

SNPs can also be scored by direct DNA sequencing. A variety of automated sequencing procedures can be utilized ((1995) Biotechniques 19:448), including sequencing by mass spectrometry (see, e.g., PCT International Publication No. WO94/16101; Cohen et al., Adv. Chromatogr. 36:127-162 (1996); and Griffin et al., Appl. Biochem. Biotechnol. 38:147-159 (1993)). The nucleic acid sequences of the present invention enable one of ordinary skill in the art to readily design sequencing primers for such automated sequencing procedures. Commercial instrumentation, such as the Applied Biosystems 377, 3100, 3700, 3730, and 3730×1 DNA Analyzers (Foster City, Calif.), is commonly used in the art for automated sequencing.

Other methods that can be used to genotype the SNPs of the present invention include single-strand conformational polymorphism (SSCP), and denaturing gradient gel electrophoresis (DGGE) (Myers et al., Nature 313:495 (1985)). SSCP identifies base differences by alteration in electrophoretic migration of single stranded PCR products, as described in Orita et al., Proc. Nat. Acad. Single-stranded PCR products can be generated by heating or otherwise denaturing double stranded PCR products. Single-stranded nucleic acids may refold or form secondary structures that are partially dependent on the base sequence. The different electrophoretic mobilities of single-stranded amplification products are related to base-sequence differences at SNP positions. DGGE differentiates SNP alleles based on the different sequence-dependent stabilities and melting properties inherent in polymorphic DNA and the corresponding differences in electrophoretic migration patterns in a denaturing gradient gel (Erlich, ed., PCR Technology, Principles and Applications for DNA Amplification, W.H. Freeman and Co, New York, 1992, Chapter 7).

Sequence-specific ribozymes (U.S. Pat. No. 5,498,531) can also be used to score SNPs based on the development or loss of a ribozyme cleavage site. Perfectly matched sequences can be distinguished from mismatched sequences by nuclease cleavage digestion assays or by differences in melting temperature. If the SNP affects a restriction enzyme cleavage site, the SNP can be identified by alterations in restriction enzyme digestion patterns, and the corresponding changes in nucleic acid fragment lengths determined by gel electrophoresis

SNP genotyping can include the steps of, for example, collecting a biological sample from a human subject (e.g., sample of tissues, cells, fluids, secretions, etc.), isolating nucleic acids (e.g., genomic DNA, mRNA or both) from the cells of the sample, contacting the nucleic acids with one or more primers which specifically hybridize to a region of the isolated nucleic acid containing a target SNP under conditions such that hybridization and amplification of the target nucleic acid region occurs, and determining the nucleotide present at the SNP position of interest, or, in some assays, detecting the presence or absence of an amplification product (assays can be designed so that hybridization and/or amplification will only occur if a particular SNP allele is present or absent). In some assays, the size of the amplification product is detected and compared to the length of a control sample; for example, deletions and insertions can be detected by a change in size of the amplified product compared to a normal genotype.

SNP genotyping is useful for numerous practical applications, as described below. Examples of such applications include, but are not limited to, SNP-disease association analysis, disease predisposition screening, disease diagnosis, disease prognosis, disease progression monitoring, determining therapeutic strategies based on an individual's genotype (“pharmacogenomics”), developing therapeutic agents based on SNP genotypes associated with a disease or likelihood of responding to a drug, stratifying a patient population for clinical trial for a treatment regimen, predicting the likelihood that an individual will experience toxic side effects from a therapeutic agent, and human identification applications such as forensics.

Analysis of Genetic Association Between SNPs and Phenotypic Traits

SNP genotyping for disease diagnosis, disease predisposition screening, disease prognosis, determining drug responsiveness (pharmacogenomics), drug toxicity screening, and other uses described herein, typically relies on initially establishing a genetic association between one or more specific SNPs and the particular phenotypic traits of interest.

Different study designs may be used for genetic association studies (Modern Epidemiology, Lippincott Williams & Wilkins (1998), 609-622). Observational studies are most frequently carried out in which the response of the patients is not interfered with. The first type of observational study identifies a sample of persons in whom the suspected cause of the disease is present and another sample of persons in whom the suspected cause is absent, and then the frequency of development of disease in the two samples is compared. These sampled populations are called cohorts, and the study is a prospective study. The other type of observational study is case-control or a retrospective study. In typical case-control studies, samples are collected from individuals with the phenotype of interest (cases) such as certain manifestations of a disease, and from individuals without the phenotype (controls) in a population (target population) that conclusions are to be drawn from. Then the possible causes of the disease are investigated retrospectively. As the time and costs of collecting samples in case-control studies are considerably less than those for prospective studies, case-control studies are the more commonly used study design in genetic association studies, at least during the exploration and discovery stage.

In both types of observational studies, there may be potential confounding factors that should be taken into consideration. Confounding factors are those that are associated with both the real cause(s) of the disease and the disease itself, and they include demographic information such as age, gender, ethnicity as well as environmental factors. When confounding factors are not matched in cases and controls in a study, and are not controlled properly, spurious association results can arise. If potential confounding factors are identified, they should be controlled for by analysis methods explained below.

In a genetic association study, the cause of interest to be tested is a certain allele or a SNP or a combination of alleles or a haplotype from several SNPs. Thus, tissue specimens (e.g., whole blood) from the sampled individuals may be collected and genomic DNA genotyped for the SNP(s) of interest. In addition to the phenotypic trait of interest, other information such as demographic (e.g., age, gender, ethnicity, etc.), clinical, and environmental information that may influence the outcome of the trait can be collected to further characterize and define the sample set. In many cases, these factors are known to be associated with diseases and/or SNP allele frequencies. There are likely gene-environment and/or gene-gene interactions as well. Analysis methods to address gene-environment and gene-gene interactions (for example, the effects of the presence of both susceptibility alleles at two different genes can be greater than the effects of the individual alleles at two genes combined) are discussed below.

After all the relevant phenotypic and genotypic information has been obtained, statistical analyses are carried out to determine if there is any significant correlation between the presence of an allele or a genotype with the phenotypic characteristics of an individual. Preferably, data inspection and cleaning are first performed before carrying out statistical tests for genetic association. Epidemiological and clinical data of the samples can be summarized by descriptive statistics with tables and graphs. Data validation is preferably performed to check for data completion, inconsistent entries, and outliers. Chi-squared tests and t-tests (Wilcoxon rank-sum tests if distributions are not normal) may then be used to check for significant differences between cases and controls for discrete and continuous variables, respectively. To ensure genotyping quality, Hardy-Weinberg disequilibrium tests can be performed on cases and controls separately. Significant deviation from Hardy-Weinberg equilibrium (HWE) in both cases and controls for individual markers can be indicative of genotyping errors. If HWE is violated in a majority of markers, it is indicative of population substructure that should be further investigated. Moreover, Hardy-Weinberg disequilibrium in cases only can indicate genetic association of the markers with the disease (Genetic Data Analysis, Weir B., Sinauer (1990)).

To test whether an allele of a single SNP is associated with the case or control status of a phenotypic trait, one skilled in the art can compare allele frequencies in cases and controls. Standard chi-squared tests and Fisher exact tests can be carried out on a 2×2 table (2 SNP alleles×2 outcomes in the categorical trait of interest). To test whether genotypes of a SNP are associated, chi-squared tests can be carried out on a 3×2 table (3 genotypes×2 outcomes). Score tests are also carried out for genotypic association to contrast the three genotypic frequencies (major homozygotes, heterozygotes and minor homozygotes) in cases and controls, and to look for trends using 3 different modes of inheritance, namely dominant (with contrast coefficients 2, −1, −1), additive (with contrast coefficients 1, 0, −1) and recessive (with contrast coefficients 1, 1, −2). Odds ratios for minor versus major alleles, and odds ratios for heterozygote and homozygote variants versus the wild type genotypes are calculated with the desired confidence limits, usually 95%.

In order to control for confounders and to test for interaction and effect modifiers, stratified analyses may be performed using stratified factors that are likely to be confounding, including demographic information such as age, ethnicity, and gender, or an interacting element or effect modifier, such as a known major gene (e.g., APOE for Alzheimer's disease or HLA genes for autoimmune diseases), or environmental factors such as smoking in lung cancer. Stratified association tests may be carried out using Cochran-Mantel-Haenszel tests that take into account the ordinal nature of genotypes with 0, 1, and 2 variant alleles. Exact tests by StatXact may also be performed when computationally possible. Another way to adjust for confounding effects and test for interactions is to perform stepwise multiple logistic regression analysis using statistical packages such as SAS or R. Logistic regression is a model-building technique in which the best fitting and most parsimonious model is built to describe the relation between the dichotomous outcome (for instance, getting a certain disease or not) and a set of independent variables (for instance, genotypes of different associated genes, and the associated demographic and environmental factors). The most common model is one in which the logit transformation of the odds ratios is expressed as a linear combination of the variables (main effects) and their cross-product terms (interactions) (Applied Logistic Regression, Hosmer and Lemeshow, Wiley (2000)). To test whether a certain variable or interaction is significantly associated with the outcome, coefficients in the model are first estimated and then tested for statistical significance of their departure from zero.

In addition to performing association tests one marker at a time, haplotype association analysis may also be performed to study a number of markers that are closely linked together. Haplotype association tests can have better power than genotypic or allelic association tests when the tested markers are not the disease-causing mutations themselves but are in linkage disequilibrium with such mutations. The test will even be more powerful if the disease is indeed caused by a combination of alleles on a haplotype (e.g., APOE is a haplotype formed by 2 SNPs that are very close to each other). In order to perform haplotype association effectively, marker-marker linkage disequilibrium measures, both D′ and R², are typically calculated for the markers within a gene to elucidate the haplotype structure. Recent studies (Daly et al, Nature Genetics, 29, 232-235, 2001) in linkage disequilibrium indicate that SNPs within a gene are organized in block pattern, and a high degree of linkage disequilibrium exists within blocks and very little linkage disequilibrium exists between blocks. Haplotype association with the disease status can be performed using such blocks once they have been elucidated.

Haplotype association tests can be carried out in a similar fashion as the allelic and genotypic association tests. Each haplotype in a gene is analogous to an allele in a multi-allelic marker. One skilled in the art can either compare the haplotype frequencies in cases and controls or test genetic association with different pairs of haplotypes. It has been proposed (Schaid et al, Am. J. Hum. Genet., 70, 425-434, 2002) that score tests can be done on haplotypes using the program “haplo.score”. In that method, haplotypes are first inferred by EM algorithm and score tests are carried out with a generalized linear model (GLM) framework that allows the adjustment of other factors.

An important decision in the performance of genetic association tests is the determination of the significance level at which significant association can be declared when the p-value of the tests reaches that level. In an exploratory analysis where positive hits will be followed up in subsequent confirmatory testing, an unadjusted p-value <0.2 (a significance level on the lenient side), for example, may be used for generating hypotheses for significant association of a SNP with certain phenotypic characteristics of a disease. It is preferred that a p-value <0.05 (a significance level traditionally used in the art) is achieved in order for a SNP to be considered to have an association with a disease. It is more preferred that a p-value <0.01 (a significance level on the stringent side) is achieved for an association to be declared. When hits are followed up in confirmatory analyses in more samples of the same source or in different samples from different sources, adjustment for multiple testing will be performed as to avoid excess number of hits while maintaining the experiment-wise error rates at 0.05. While there are different methods to adjust for multiple testing to control for different kinds of error rates, a commonly used but rather conservative method is Bonferroni correction to control the experiment-wise or family-wise error rate (Multiple comparisons and multiple tests, Westfall et al, SAS Institute (1999)). Permutation tests to control for the false discovery rates, FDR, can be more powerful (Benjamini and Hochberg, Journal of the Royal Statistical Society, Series B 57, 1289-1300, 1995, Resampling-based Multiple Testing, Westfall and Young, Wiley (1993)). Such methods to control for multiplicity would be preferred when the tests are dependent and controlling for false discovery rates is sufficient as opposed to controlling for the experiment-wise error rates.

In replication studies using samples from different populations after statistically significant markers have been identified in the exploratory stage, meta-analyses can then be performed by combining evidence of different studies (Modern Epidemiology, Lippincott Williams & Wilkins, 1998, 643-673). If available, association results known in the art for the same SNPs can be included in the meta-analyses.

Since both genotyping and disease status classification can involve errors, sensitivity analyses may be performed to see how odds ratios and p-values would change upon various estimates on genotyping and disease classification error rates.

It has been well known that subpopulation-based sampling bias between cases and controls can lead to spurious results in case-control association studies (Ewens and Spielman, Am. J. Hum. Genet. 62, 450-458, 1995) when prevalence of the disease is associated with different subpopulation groups. Such bias can also lead to a loss of statistical power in genetic association studies. To detect population stratification, Pritchard and Rosenberg (Pritchard et al. Am. J. Hum. Gen. 1999, 65:220-228) suggested typing markers that are unlinked to the disease and using results of association tests on those markers to determine whether there is any population stratification. When stratification is detected, the genomic control (GC) method as proposed by Devlin and Roeder (Devlin et al. Biometrics 1999, 55:997-1004) can be used to adjust for the inflation of test statistics due to population stratification. GC method is robust to changes in population structure levels as well as being applicable to DNA pooling designs (Devlin et al. Genet. Epidem. 20001, 21:273-284).

While Pritchard's method recommended using 15-20 unlinked microsatellite markers, it suggested using more than 30 biallelic markers to get enough power to detect population stratification. For the GC method, it has been shown (Bacanu et al. Am. J. Hum. Genet. 2000, 66:1933-1944) that about 60-70 biallelic markers are sufficient to estimate the inflation factor for the test statistics due to population stratification. Hence, 70 intergenic SNPs can be chosen in unlinked regions as indicated in a genome scan (Kehoe et al. Hum. Mol. Genet. 1999, 8:237-245).

Once individual risk factors, genetic or non-genetic, have been found for the predisposition to disease, the next step is to set up a classification/prediction scheme to predict the category (for instance, disease or no-disease) that an individual will be in depending on his genotypes of associated SNPs and other non-genetic risk factors. Logistic regression for discrete trait and linear regression for continuous trait are standard techniques for such tasks (Applied Regression Analysis, Draper and Smith, Wiley (1998)). Moreover, other techniques can also be used for setting up classification. Such techniques include, but are not limited to, MART, CART, neural network, and discriminant analyses that are suitable for use in comparing the performance of different methods (The Elements of Statistical Learning, Hastie, Tibshirani & Friedman, Springer (2002)).

Disease Diagnosis and Predisposition Screening

Information on association/correlation between genotypes and disease-related phenotypes can be exploited in several ways. For example, in the case of a highly statistically significant association between one or more SNPs with predisposition to a disease for which treatment is available, detection of such a genotype pattern in an individual may justify immediate administration of treatment, or at least the institution of regular monitoring of the individual. Detection of the susceptibility alleles associated with serious disease in a couple contemplating having children may also be valuable to the couple in their reproductive decisions. In the case of a weaker but still statistically significant association between a SNP and a human disease, immediate therapeutic intervention or monitoring may not be justified after detecting the susceptibility allele or SNP. Nevertheless, the subject can be motivated to begin simple life-style changes (e.g., diet, exercise) that can be accomplished at little or no cost to the individual but would confer potential benefits in reducing the risk of developing conditions for which that individual may have an increased risk by virtue of having the susceptibility allele(s).

The SNPs of the invention may contribute to psoriasis and related pathologies in an individual in different ways. Some polymorphisms occur within a protein coding sequence and contribute to disease phenotype by affecting protein structure. Other polymorphisms occur in noncoding regions but may exert phenotypic effects indirectly via influence on, for example, replication, transcription, and/or translation. A single SNP may affect more than one phenotypic trait. Likewise, a single phenotypic trait may be affected by multiple SNPs in different genes.

As used herein, the terms “diagnose”, “diagnosis”, and “diagnostics” include, but are not limited to any of the following: detection of psoriasis that an individual may presently have, predisposition/susceptibility screening (i.e., determining the increased risk of an individual in developing psoriasis in the future, or determining whether an individual has a decreased risk of developing psoriasis in the future, determining a particular type or subclass of psoriasis in an individual known to have psoriasis, confirming or reinforcing a previously made diagnosis of psoriasis, pharmacogenomic evaluation of an individual to determine which therapeutic strategy that individual is most likely to positively respond to or to predict whether a patient is likely to respond to a particular treatment, predicting whether a patient is likely to experience toxic effects from a particular treatment or therapeutic compound, and evaluating the future prognosis of an individual having psoriasis. Such diagnostic uses are based on the SNPs individually or in a unique combination or SNP haplotypes of the present invention.

Haplotypes are particularly useful in that, for example, fewer SNPs can be genotyped to determine if a particular genomic region harbors a locus that influences a particular phenotype, such as in linkage disequilibrium-based SNP association analysis.

Linkage disequilibrium (LD) refers to the co-inheritance of alleles (e.g., alternative nucleotides) at two or more different SNP sites at frequencies greater than would be expected from the separate frequencies of occurrence of each allele in a given population. The expected frequency of co-occurrence of two alleles that are inherited independently is the frequency of the first allele multiplied by the frequency of the second allele. Alleles that co-occur at expected frequencies are said to be in “linkage equilibrium”. In contrast, LD refers to any non-random genetic association between allele(s) at two or more different SNP sites, which is generally due to the physical proximity of the two loci along a chromosome. LD can occur when two or more SNPs sites are in close physical proximity to each other on a given chromosome and therefore alleles at these SNP sites will tend to remain unseparated for multiple generations with the consequence that a particular nucleotide (allele) at one SNP site will show a non-random association with a particular nucleotide (allele) at a different SNP site located nearby. Hence, genotyping one of the SNP sites will give almost the same information as genotyping the other SNP site that is in LD.

Various degrees of LD can be encountered between two or more SNPs with the result being that some SNPs are more closely associated (i.e., in stronger LD) than others. Furthermore, the physical distance over which LD extends along a chromosome differs between different regions of the genome, and therefore the degree of physical separation between two or more SNP sites necessary for LD to occur can differ between different regions of the genome.

For diagnostic purposes and similar uses, if a particular SNP site is found to be useful for diagnosing psoriasis and related pathologies (e.g., has a significant statistical association with the condition and/or is recognized as a causative polymorphism for the condition), then the skilled artisan would recognize that other SNP sites which are in LD with this SNP site would also be useful for diagnosing the condition. Thus, polymorphisms (e.g., SNPs and/or haplotypes) that are not the actual disease-causing (causative) polymorphisms, but are in LD with such causative polymorphisms, are also useful. In such instances, the genotype of the polymorphism(s) that is/are in LD with the causative polymorphism is predictive of the genotype of the causative polymorphism and, consequently, predictive of the phenotype (e.g., psoriasis) that is influenced by the causative SNP(s). Therefore, polymorphic markers that are in LD with causative polymorphisms are useful as diagnostic markers, and are particularly useful when the actual causative polymorphism(s) is/are unknown.

Examples of polymorphisms that can be in LD with one or more causative polymorphisms (and/or in LD with one or more polymorphisms that have a significant statistical association with a condition) and therefore useful for diagnosing the same condition that the causative/associated SNP(s) is used to diagnose, include, for example, other SNPs in the same gene, protein-coding, or mRNA transcript-coding region as the causative/associated SNP, other SNPs in the same exon or same intron as the causative/associated SNP, other SNPs in the same haplotype block as the causative/associated SNP, other SNPs in the same intergenic region as the causative/associated SNP, SNPs that are outside but near a gene (e.g., within 6 kb on either side, 5′ or 3′, of a gene boundary) that harbors a causative/associated SNP, etc. Such useful LD SNPs can be selected from among the SNPs disclosed in Tables 1-4, for example.

Linkage disequilibrium in the human genome is reviewed in the following references: Wall et al. et al., “Haplotype blocks and linkage disequilibrium in the human genome,”, Nat Rev Genet. 2003 August; 4(8):587-97 (August 2003); Garner et al. et al., “On selecting markers for association studies: patterns of linkage disequilibrium between two and three diallelic loci,”, Genet Epidemiol. 2003 January; 24(1):57-67 (January 2003); Ardlie et al et al., “Patterns of linkage disequilibrium in the human genome,”, Nat Rev Genet. 2002 April; 3(4):299-309 (April 2002); (erratum in Nat Rev Genet. 2002 July; 3(7):566 (July 2002); and Remm et al et al., “High-density genotyping and linkage disequilibrium in the human genome using chromosome 22 as a model,”; Curr Opin Chem. Biol. 2002 February; 6(1):24-30 (February 2002); J. B. S. Haldane, “JBS (1919) The combination of linkage values, and the calculation of distances between the loci of linked factors,”. J Genet. 8:299-309 (1919); G. Mendel, G. (1866) Versuche über Pflanzen-Hybriden. Verhandlungen des natur orschenden Vereines in Brünn [(Proceedings of the Natural History Society of Brünn)] (1866); Lewin B (1990) Genes IV, B. Lewin, ed., Oxford University Press, N.Y. New York, USA (1990); D. L. Hartl DL and A. G. Clark AG (1989) Principles of Population Genetics 2^(nd) ed., Sinauer Associates, Inc., Ma Sunderland, Mass., USA (1989); J. H. Gillespie JH (2004) Population Genetics: A Concise Guide.2^(nd) ed., Johns Hopkins University Press. (2004) USA; R. C. Lewontin, “RC (1964) The interaction of selection and linkage. I. General considerations; heterotic models,”. Genetics 49:49-67 (1964); P. G. Hoel, P G (1954) Introduction to Mathematical Statistics 2^(nd) ed., John Wiley & Sons, Inc., N.Y. New York, USA (1954); R. R. Hudson, R R “(2001) Two-locus sampling distributions and their application,”. Genetics 159:1805-1817 (2001); A. P. Dempster A P, N. M. Laird, D. B. N M, Rubin, “DB (1977) Maximum likelihood from incomplete data via the EM algorithm,”. J R Stat Soc 39:1-48 (1977); L. Excoffier L, M. Slatkin, M “(1995) Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population,”. Mol Biol Evol 12(5):921-927 (1995); D. A. Tregouet DA, S. Escolano S, L. Tiret L, A. Mallet A, J. L. Golmard, J L “(2004) A new algorithm for haplotype-based association analysis: the Stochastic-EM algorithm,”. Ann Hum Genet. 68(Pt 2):165-177 (2004); A. D. Long AD and C. H. Langley CH, “(1999) The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits,”. Genome Research 9:720-731 (1999); A. Agresti, A (1990) Categorical Data Analysis, John Wiley & Sons, Inc., N.Y. New York, USA (1990); K. Lange, K (1997) Mathematical and Statistical Methods for Genetic Analysis, Springer-Verlag New York, Inc., N.Y. New York, USA (1997); The International HapMap Consortium, “(2003) The International HapMap Project,”. Nature 426:789-796 (2003); The International HapMap Consortium, “(2005) A haplotype map of the human genome,”. Nature 437:1299-1320 (2005); G. A. Thorisson G A, A. V. Smith A V, L. Krishnan L, L. D. Stein L D (2005), “The International HapMap Project Web Site,”. Genome Research 15:1591-1593 (2005); G. McVean, C. C. A. G, Spencer C C A, R. Chaix R (2005), “Perspectives on human genetic variation from the HapMap project,”. PLoS Genetics 1(4):413-418 (2005); J. N. Hirschhorn J N, M. J. Daly, M J “(2005) Genome-wide association studies for common diseases and complex traits,”. Nat Genet. 6:95-108 (2005); S. J. Schrodi, “SJ (2005) A probabilistic approach to large-scale association scans: a semi-Bayesian method to detect disease-predisposing alleles,”. SAGMB 4(1):31 (2005); W. Y. S. Wang W Y S, B. J. Barratt B J, D. G. Clayton DG, J. A. Todd, “J A (2005) Genome-wide association studies: theoretical and practical concerns,”. Nat Rev Genet. 6:109-118 (2005); J. K. Pritchard J K, M. Przeworski, “M (2001) Linkage disequilibrium in humans: models and data,”. Am J Hum Genet. 69:1-14 (2001).

As discussed above, one aspect of the present invention is the discovery that SNPs which are in certain LD distance with the interrogated SNP can also be used as valid markers for identifying an increased or decreased risks of having or developing psoriasis. As used herein, the term “interrogated SNP” refers to SNPs that have been found to be associated with an increased or decreased risk of disease using genotyping results and analysis, or other appropriate experimental method as exemplified in the working examples described in this application. As used herein, the term “LD SNP” refers to a SNP that has been characterized as a SNP associating with an increased or decreased risk of diseases due to their being in LD with the “interrogated SNP” under the methods of calculation described in the application. Below, applicants describe the methods of calculation with which one of ordinary skilled in the art may determine if a particular SNP is in LD with an interrogated SNP. The parameter r² is commonly used in the genetics art to characterize the extent of linkage disequilibrium between markers (Hudson, 2001). As used herein, the term “in LD with” refers to a particular SNP that is measured at above the threshold of a parameter such as r² with an interrogated SNP.

It is now common place to directly observe genetic variants in a sample of chromosomes obtained from a population. Suppose one has genotype data at two genetic markers located on the same chromosome, for the markers A and B. Further suppose that two alleles segregate at each of these two markers such that alleles A₁ and A₂ can be found at marker A and alleles B₁ and B₂ at marker B. Also assume that these two markers are on a human autosome. If one is to examine a specific individual and find that they are heterozygous at both markers, such that their two-marker genotype is A₁A₂B₁B₂, then there are two possible configurations: the individual in question could have the alleles A₁B₁ on one chromosome and A₂B₂ on the remaining chromosome; alternatively, the individual could have alleles A₁B₂ on one chromosome and A₂B₁ on the other. The arrangement of alleles on a chromosome is called a haplotype. In this illustration, the individual could have haplotypes A₁B₁/A₂B₂ or A₁B₂/A₂B₁ (see Hartl and Clark (1989) for a more complete description). The concept of linkage equilibrium relates the frequency of haplotypes to the allele frequencies.

Assume that a sample of individuals is selected from a larger population. Considering the two markers described above, each having two alleles, there are four possible haplotypes: A₁B₁, A₁B₂, A₂B₁ and A₂B₂. Denote the frequencies of these four haplotypes with the following notation.

P ₁₁=freq(A ₁ B ₁)  (1)

P ₁₂=freq(A ₁ B ₂)  (2)

P ₂₁=freq(A ₂ B ₁)  (3)

P ₂₂=freq(A ₂ B ₂)  (4)

The allele frequencies at the two markers are then the sum of different haplotype frequencies, it is straightforward to write down a similar set of equations relating single-marker allele frequencies to two-marker haplotype frequencies:

p ₁=freq(A ₁)=P ₁₁ +P ₁₂  (5)

p ₂=freq(A ₂)=P ₂₁ +P ₂₂  (6)

q ₁=freq(B ₁)=P ₁₁ +P ₂₁  (7)

q ₂=freq(B ₂)=P ₁₂ +P ₂₂  (8)

Note that the four haplotype frequencies and the allele frequencies at each marker must sum to a frequency of 1.

P ₁₁ +P ₁₂ +P ₂₁ +P ₂₂=1  (9)

p ₁ +p ₂=1  (10)

q ₁ +q ₂=1  (11)

If there is no correlation between the alleles at the two markers, one would expect that the frequency of the haplotypes would be approximately the product of the composite alleles. Therefore,

P₁₁≈p₁q₁  (12)

P₁₂≈p₁q₂  (13)

P₂₁≈p₂q₁  (14)

P₂₂≈p₂q₂  (15)

These approximating equations (12)-(15) represent the concept of linkage equilibrium where there is independent assortment between the two markers—the alleles at the two markers occur together at random. These are represented as approximations because linkage equilibrium and linkage disequilibrium are concepts typically thought of as properties of a sample of chromosomes; and as such they are susceptible to stochastic fluctuations due to the sampling process. Empirically, many pairs of genetic markers will be in linkage equilibrium, but certainly not all pairs.

Having established the concept of linkage equilibrium above, applicants can now describe the concept of linkage disequilibrium (LD), which is the deviation from linkage equilibrium. Since the frequency of the A₁B₁ haplotype is approximately the product of the allele frequencies for A₁ and B₁ under the assumption of linkage equilibrium as stated mathematically in (12), a simple measure for the amount of departure from linkage equilibrium is the difference in these two quantities, D,

D=P ₁₁ −p ₁ q ₁  (16)

D=0 indicates perfect linkage equilibrium. Substantial departures from D=0 indicates LD in the sample of chromosomes examined. Many properties of D are discussed in Lewontin (1964) including the maximum and minimum values that D can take. Mathematically, using basic algebra, it can be shown that D can also be written solely in terms of haplotypes:

D=P ₁₁ P ₂₂ −P ₁₂ P ₂₁  (17)

If one transforms D by squaring it and subsequently dividing by the product of the allele frequencies of A₁, A₂, B₁ and B₂, the resulting quantity, called r², is equivalent to the square of the Pearson's correlation coefficient commonly used in statistics (e.g. Hoel, 1954).

$\begin{matrix} {r^{2} = \frac{D^{2}}{p_{1}p_{2}q_{1}q_{2}}} & (18) \end{matrix}$

As with D, values of r² close to 0 indicate linkage equilibrium between the two markers examined in the sample set. As values of r² increase, the two markers are said to be in linkage disequilibrium. The range of values that r² can take are from 0 to 1. r²=1 when there is a perfect correlation between the alleles at the two markers.

In addition, the quantities discussed above are sample-specific. And as such, it is necessary to formulate notation specific to the samples studied. In the approach discussed here, three types of samples are of primary interest: (i) a sample of chromosomes from individuals affected by a disease-related phenotype (cases), (ii) a sample of chromosomes obtained from individuals not affected by the disease-related phenotype (controls), and (iii) a standard sample set used for the construction of haplotypes and calculation pairwise linkage disequilibrium. For the allele frequencies used in the development of the method described below, an additional subscript will be added to denote either the case or control sample sets.

p _(1.es)=freq(A ₁ in cases)  (19)

p _(2.es)=freq(A ₂ in cases)  (20)

q _(1.es)=freq(B ₁ in cases)  (21)

q _(2.es)=freq(B ₂ in cases)  (22)

Similarly,

p _(1.es)=freq(A ₁ in controls)  (23)

p _(2.es)=freq(A ₂ in controls)  (24)

q _(1.es)=freq(B ₁ in controls)  (25)

q _(2.es)=freq(B ₂ in controls)  (26)

As a well-accepted sample set is necessary for robust linkage disequilibrium calculations, data obtained from the International HapMap project (The International HapMap Consortium 2003, 2005; Thorisson et al, 2005; McVean et al, 2005) can be used for the calculation of pairwise r² values. Indeed, the samples genotyped for the International HapMap Project were selected to be representative examples from various human sub-populations with sufficient numbers of chromosomes examined to draw meaningful and robust conclusions from the patterns of genetic variation observed. The International HapMap project website (hapmap.org) contains a description of the project, methods utilized and samples examined. It is useful to examine empirical data to get a sense of the patterns present in such data.

Haplotype frequencies were explicit arguments in equation (18) above. However, knowing the 2-marker haplotype frequencies requires that phase to be determined for doubly heterozygous samples. When phase is unknown in the data examined, various algorithms can be used to infer phase from the genotype data. This issue was discussed earlier where the doubly heterozygous individual with a 2-SNP genotype of A₁A₂B₁B₂ could have one of two different sets of chromosomes: A₁B₁/A₂B₂ or A₁B₂/A₂B₁. One such algorithm to estimate haplotype frequencies is the expectation-maximization (EM) algorithm first formalized by Dempster et. al et al. (1977). This algorithm is often used in genetics to infer haplotype frequencies from genotype data (e.g. e.g. Excoffier and Slatkin, (1995); Tregouet et al et al., (2004)). It should be noted that for the two-SNP case explored here, EM algorithms have very little error provided that the allele frequencies and sample sizes are not too small. The impact on r² values is typically negligible.

As correlated genetic markers share information, interrogation of SNP markers in LD with a disease-associated SNP marker can also have sufficient power to detect disease association (Long and Langley, (1999)). The relationship between the power to directly find disease-associated alleles and the power to indirectly detect disease-association was investigated by Pritchard and Przeworski (2001). In a straight-forward derivation, it can be shown that the power to detect disease association indirectly at a marker locus in linkage disequilibrium with a disease-association locus is approximately the same as the power to detect disease-association directly at the disease-association locus if the sample size is increased by a factor of

$\frac{1}{r^{2}}$

(the reciprocal of equation 18) at the marker in comparison with the disease-association locus.

Therefore, if one calculated the power to detect disease-association indirectly with an experiment having N samples, then equivalent power to directly detect disease-association (at the actual disease-susceptibility locus) would necessitate an experiment using approximately r²N samples. This elementary relationship between power, sample size and linkage disequilibrium can be used to derive an r² threshold value useful in determining whether or not genotyping markers in linkage disequilibrium with a SNP marker directly associated with disease status has enough power to indirectly detect disease-association.

To commence a derivation of the power to detect disease-associated markers through an indirect process, define the effective chromosomal sample size as

$\begin{matrix} {{n = \frac{4N_{cs}N_{ct}}{N_{cs} + N_{ct}}};} & (27) \end{matrix}$

where N_(cs) and N_(ct) are the numbers of diploid cases and controls, respectively. This is necessary to handle situations where the numbers of cases and controls are not equivalent. For equal case and control sample sizes, N_(cs)=N_(ct)=N, the value of the effective number of chromosomes is simply n=2N—as expected. Let power be calculated for a significance level α (such that traditional P-values below α will be deemed statistically significant). Define the standard Gaussian distribution function as Φ(•). Mathematically,

$\begin{matrix} {{\Phi (x)} = {\frac{1}{\sqrt{2\pi}}{\int_{- \infty}^{x}{^{- \frac{\theta^{2}}{2}}\ {\theta}}}}} & (28) \end{matrix}$

Alternatively, the following error function notation (Erf) may also be used,

$\begin{matrix} {{\Phi (x)} = {\frac{1}{2}\left\lbrack {1 + {{Erf}\left( \frac{x}{\sqrt{2}} \right)}} \right\rbrack}} & (29) \end{matrix}$

For example, Φ(11.644854)=0.95. The value of r² may be derived to yield a pre-specified minimum amount of power to detect disease association though indirect interrogation. Noting that the LD SNP marker could be the one that is carrying the disease- association allele, therefore that this approach constitutes a lower-bound model where all indirect power results are expected to be at least as large as those interrogated.

Denote by β the error rate for not detecting truly disease-associated markers. Therefore, 1−β, is the classical definition of statistical power. Substituting the Pritchard-Pzreworski result into the sample size, the power to detect disease association at a significance level of α is given by the approximation

$\begin{matrix} {{{1 - \beta}\; \cong \; {\Phi\left\lbrack {\frac{{q_{1,{cs}} - q_{1,{ct}}}}{\sqrt{\frac{{q_{1,{cs}}\left( {1 - q_{1,{cs}}} \right)} + {q_{1,{ct}}\left( {1 - q_{1,{ct}}} \right)}}{r^{2}n}}} - Z_{1 - {\alpha/2}}} \right\rbrack}};} & (30) \end{matrix}$

where Z_(u) is the inverse of the standard normal cumulative distribution evaluated at u (uε(0,1)). Z_(u)=Φ⁻¹(u), where Φ(Φ⁻¹(u))=Φ⁻¹(Φ(u))=u. For example, setting α=0.05, and therefore 1−α/3=0.975, Z_(0.975)=1.95996 is obtained. Next, setting power equal to a threshold of a minimum power of T,

$\begin{matrix} {T\; = \; {\Phi\left\lbrack {\frac{{q_{1,{cs}} - q_{1,{ct}}}}{\sqrt{\frac{{q_{1,{cs}}\left( {1 - q_{1,{cs}}} \right)} + {q_{1,{ct}}\left( {1 - q_{1,{ct}}} \right)}}{r^{2}n}}} - Z_{1 - {\alpha/2}}} \right\rbrack}} & (31) \end{matrix}$

and solving for r², the following threshold r² is obtained:

$\begin{matrix} {r_{T}^{2} = {\frac{\left\lbrack {{q_{1,{cs}}\left( {1 - q_{1,{cs}}} \right)} + {q_{1,{ct}}\left( {1 - q_{1,{ct}}} \right)}} \right\rbrack}{{n\left( {q_{1,{cs}} - q_{1,{ct}}} \right)}^{2}}\left\lbrack {{\Phi^{- 1}(T)} + Z_{1 - {a/2}}} \right\rbrack}^{2}} & (32) \end{matrix}$

Or,

$\begin{matrix} {r_{T}^{2} = {\frac{\left( {Z_{T} + Z_{1 - {\alpha/2}}} \right)^{2}}{n}\left\lbrack \frac{q_{1,{cs}} - \left( q_{1,{cs}} \right)^{2} + q_{1,{ct}} - \left( q_{1,{ct}} \right)^{2}}{\left( {q_{1,{cs}} - q_{1,{ct}}} \right)^{2}} \right\rbrack}} & (33) \end{matrix}$

Suppose that r² is calculated between an interrogated SNP and a number of other SNPs with varying levels of LD with the interrogated SNP. The threshold value r_(T) ² is the minimum value of linkage disequilibrium between the interrogated SNP and the potential LD SNPs such that the LD SNP still retains a power greater or equal to T for detecting disease-association. For example, suppose that SNP rs200 is genotyped in a case-control disease-association study and it is found to be associated with a disease phenotype. Further suppose that the minor allele frequency in 1,000 case chromosomes was found to be 16% in contrast with a minor allele frequency of 10% in 1,000 control chromosomes. Given those measurements one could have predicted, prior to the experiment, that the power to detect disease association at a significance level of 0.05 was quite high—approximately 98% using a test of allelic association. Applying equation (32) one can calculate a minimum value of r² to indirectly assess disease association assuming that the minor allele at SNP rs200 is truly disease-predisposing for a threshold level of power. If one sets the threshold level of power to be 80%, then r_(T) ²=0.489 given the same significance level and chromosome numbers as above. Hence, any SNP with a pairwise r² value with rs200 greater than 0.489 is expected to have greater than 80% power to detect the disease association. Further, this is assuming the conservative model where the LD SNP is disease-associated only through linkage disequilibrium with the interrogated SNP rs200.

The contribution or association of particular SNPs and/or SNP haplotypes with disease phenotypes, such as psoriasis, enables the SNPs of the present invention to be used to develop superior diagnostic tests capable of identifying individuals who express a detectable trait, such as psoriasis, as the result of a specific genotype, or individuals whose genotype places them at an increased or decreased risk of developing a detectable trait at a subsequent time as compared to individuals who do not have that genotype. As described herein, diagnostics may be based on a single SNP or a group of SNPs. Combined detection of a plurality of SNPs (for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 30, 32, 48, 50, 64, 96, 100, or any other number in-between, or more, of the SNPs provided in Table 1 and/or Table 2) typically increases the probability of an accurate diagnosis. For example, the presence of a single SNP known to correlate with psoriasis might indicate a probability of 20% that an individual has or is at risk of developing psoriasis, whereas detection of five SNPs, each of which correlates with psoriasis, might indicate a probability of 80% that an individual has or is at risk of developing psoriasis. To further increase the accuracy of diagnosis or predisposition screening, analysis of the SNPs of the present invention can be combined with that of other polymorphisms or other risk factors of psoriasis, such as disease symptoms, pathological characteristics, family history, diet, environmental factors or lifestyle factors.

It will, of course, be understood by practitioners skilled in the treatment or diagnosis of psoriasis that the present invention generally does not intend to provide an absolute identification of individuals who are at risk (or less at risk) of developing psoriasis, and/or pathologies related to psoriasis, but rather to indicate a certain increased (or decreased) degree or likelihood of developing the disease based on statistically significant association results. However, this information is extremely valuable as it can be used to, for example, initiate preventive treatments or to allow an individual carrying one or more significant SNPs or SNP haplotypes to foresee warning signs such as minor clinical symptoms, or to have regularly scheduled physical exams to monitor for appearance of a condition in order to identify and begin treatment of the condition at an early stage. Particularly with diseases that are extremely debilitating or fatal if not treated on time, the knowledge of a potential predisposition, even if this predisposition is not absolute, would likely contribute in a very significant manner to treatment efficacy.

The diagnostic techniques of the present invention may employ a variety of methodologies to determine whether a test subject has a SNP or a SNP pattern associated with an increased or decreased risk of developing a detectable trait or whether the individual suffers from a detectable trait as a result of a particular polymorphism/mutation, including, for example, methods which enable the analysis of individual chromosomes for haplotyping, family studies, single sperm DNA analysis, or somatic hybrids. The trait analyzed using the diagnostics of the invention may be any detectable trait that is commonly observed in pathologies and disorders related to psoriasis.

Another aspect of the present invention relates to a method of determining whether an individual is at risk (or less at risk) of developing one or more traits or whether an individual expresses one or more traits as a consequence of possessing a particular trait-causing or trait-influencing allele. These methods generally involve obtaining a nucleic acid sample from an individual and assaying the nucleic acid sample to determine which nucleotide(s) is/are present at one or more SNP positions, wherein the assayed nucleotide(s) is/are indicative of an increased or decreased risk of developing the trait or indicative that the individual expresses the trait as a result of possessing a particular trait-causing or trait-influencing allele.

In another embodiment, the SNP detection reagents of the present invention are used to determine whether an individual has one or more SNP allele(s) affecting the level (e.g., the concentration of mRNA or protein in a sample, etc.) or pattern (e.g., the kinetics of expression, rate of decomposition, stability profile, Km, Vmax, etc.) of gene expression (collectively, the “gene response” of a cell or bodily fluid). Such a determination can be accomplished by screening for mRNA or protein expression (e.g., by using nucleic acid arrays, RT-PCR, TaqMan assays, or mass spectrometry), identifying genes having altered expression in an individual, genotyping SNPs disclosed in Table 1 and/or Table 2 that could affect the expression of the genes having altered expression (e.g., SNPs that are in and/or around the gene(s) having altered expression, SNPs in regulatory/control regions, SNPs in and/or around other genes that are involved in pathways that could affect the expression of the gene(s) having altered expression, or all SNPs could be genotyped), and correlating SNP genotypes with altered gene expression. In this manner, specific SNP alleles at particular SNP sites can be identified that affect gene expression.

Pharmacogenomics and Therapeutics/Drug Development

The present invention provides methods for assessing the pharmacogenomics of a subject harboring particular SNP alleles or haplotypes or diplotypes to a particular therapeutic agent or pharmaceutical compound, or to a class of such compounds. Pharmacogenomics deals with the roles which clinically significant hereditary variations (e.g., SNPs) play in the response to drugs due to altered drug disposition and/or abnormal action in affected persons. See, e.g., Roses, Nature 405, 857-865 (2000); Gould Rothberg, Nature Biotechnology 19, 209-211 (2001); Eichelbaum, Clin. Exp. Pharmacol. Physiol. 23(10-11):983-985 (1996); and Linder, Clin. Chem. 43(2):254-266 (1997). The clinical outcomes of these variations can result in severe toxicity of therapeutic drugs in certain individuals or therapeutic failure of drugs in certain individuals as a result of individual variation in metabolism. Thus, the SNP genotype of an individual can determine the way a therapeutic compound acts on the body or the way the body metabolizes the compound. For example, SNPs in drug metabolizing enzymes can affect the activity of these enzymes, which in turn can affect both the intensity and duration of drug action, as well as drug metabolism and clearance.

The discovery of SNPs in drug metabolizing enzymes, drug transporters, proteins for pharmaceutical agents, and other drug targets has explained why some patients do not obtain the expected drug effects, show an exaggerated drug effect, or experience serious toxicity from standard drug dosages. SNPs can be expressed in the phenotype of the extensive metabolizer and in the phenotype of the poor metabolizer. Accordingly, SNPs may lead to allelic variants of a protein in which one or more of the protein functions in one population are different from those in another population. SNPs and the encoded variant peptides thus provide targets to ascertain a genetic predisposition that can affect treatment modality. For example, in a ligand-based treatment, SNPs may give rise to amino terminal extracellular domains and/or other ligand-binding regions of a receptor that are more or less active in ligand binding, thereby affecting subsequent protein activation. Accordingly, ligand dosage would necessarily be modified to maximize the therapeutic effect within a given population containing particular SNP alleles or haplotypes.

As an alternative to genotyping, specific variant proteins containing variant amino acid sequences encoded by alternative SNP alleles could be identified. Thus, pharmacogenomic characterization of an individual permits the selection of effective compounds and effective dosages of such compounds for prophylactic or therapeutic uses based on the individual's SNP genotype, thereby enhancing and optimizing the effectiveness of the therapy. Furthermore, the production of recombinant cells and transgenic animals containing particular SNPs/haplotypes allow effective clinical design and testing of treatment compounds and dosage regimens. For example, transgenic animals can be produced that differ only in specific SNP alleles in a gene that is orthologous to a human disease susceptibility gene.

Pharmacogenomic uses of the SNPs of the present invention provide several significant advantages for patient care, particularly in treating psoriasis. Pharmacogenomic characterization of an individual, based on an individual's SNP genotype, can identify those individuals unlikely to respond to treatment with a particular medication and thereby allows physicians to avoid prescribing the ineffective medication to those individuals. On the other hand, SNP genotyping of an individual may enable physicians to select the appropriate medication and dosage regimen that will be most effective based on an individual's SNP genotype. This information increases a physician's confidence in prescribing medications and motivates patients to comply with their drug regimens. Furthermore, pharmacogenomics may identify patients predisposed to toxicity and adverse reactions to particular drugs or drug dosages. Adverse drug reactions lead to more than 100,000 avoidable deaths per year in the United States alone and therefore represent a significant cause of hospitalization and death, as well as a significant economic burden on the healthcare system (Pfost et. al., Trends in Biotechnology, August 2000.). Thus, pharmacogenomics based on the SNPs disclosed herein has the potential to both save lives and reduce healthcare costs substantially.

Attempts have been made to develop drugs that can be used to treat psoriasis. Several drug candidates have been introduced into clinical trials to test their efficacy in treating psoriasis. Among them are ABT-874, STA-5326, and CNTO-1275. For refences discussing these drugs, please see Burakoff, et al., A phase 1/2A trial of STA 5326, an oral interleukin-12/23 inhibitor, in patients with active moderate to severe Crohn's disease. Inflamm Bowel Dis. 2006 July; 12(7):558-65, and Borchardt J K, Focus on small molecule inhibitors for treatment of inflammatory and autoimmune diseases. Drug News Perspect. 2004 November; 17(9):607-14 (for STA-5326); Sandborn W J, How future tumor necrosis factor antagonists and other compounds will meet the remaining challenges in Crohn's disease. Rev Gastroenterol Disord. 2004; 4 Suppl 3:S25-33 (for ABT-874); Papp K A. Potential future therapies for psoriasis. Semin Cutan Med. Surg. 2005 March; 24(1):58-63 (for CNTO-1275). These drug candidates target the metabolic pathway involving IL-12 and IL-23 genes.

It is also well known in the art that markers that are diagnostically useful in distinguishing patients at higher risk of developing a disease, such as psoriasis from those who are at a decreased risk of developing psoriasis can be useful in identifying those patients that are more likely to respond to drug treatments targeting at those pathways involving genes where the diagnostic SNPs reside. See references Gerdes, et al., Circulation, 2000; 101:1366-1371, Kuivenhoven, et al., N Engl J Med 1998; 338:86-93, Stolarz, et al., Hypertension 2004; 44:156-162, Chartier-Harlin, et al., Hum. Mol. Genet. 1994 April; 3(4):569-74, Roses, et al., The Pharmacogenomics Journal 2006, 1-19.

In that regard, embodiments of the present invention can be very useful in assisting clinicians select patients who are more likely to develop psoriasis, and are in turn good candidates for drug responses targeting psoriasis, thus warrant the application of the above-mentioned drug treatments on such patients. In the mean time, patients who are deemed to have low risk of developing psoriasis, using SNP markers discovered herein, can be spared of the aggravation and wastefulness of the drug due to the reduced benefit of such treatment in view of its cost and side effect.

Pharmacogenomics in general is discussed further in Rose et al., “Pharmacogenetic analysis of clinically relevant genetic polymorphisms”, Methods Mol. Med. 2003; 85:225-37. Pharmacogenomics as it relates to Alzheimer's disease and other neurodegenerative disorders is discussed in Cacabelos, “Pharmacogenomics for the treatment of dementia”, Ann Med. 2002; 34(5):357-79, Maimone et al., “Pharmacogenomics of neurodegenerative diseases”, Eur J Pharmacol. 2001 Feb. 9; 413(1): 11-29, and Poirier, “Apolipoprotein E: a pharmacogenetic target for the treatment of Alzheimer's disease”, Mol. Diagn. 1999 December; 4(4):335-41. Pharmacogenomics as it relates to cardiovascular disorders is discussed in Siest et al., “Pharmacogenomics of drugs affecting the cardiovascular system”, Clin Chem Lab Med. 2003 April; 41(4):590-9, Mukherjee et al., “Pharmacogenomics in cardiovascular diseases”, Prog Cardiovasc Dis. 2002 May-June; 44(6):479-98, and Mooser et al., “Cardiovascular pharmacogenetics in the SNP era”, J Thromb Haemost. 2003 July; 1(7):1398-402. Pharmacogenomics as it relates to cancer is discussed in McLeod et al., “Cancer pharmacogenomics: SNPs, chips, and the individual patient”, Cancer Invest. 2003; 21(4):630-40 and Watters et al., “Cancer pharmacogenomics: current and future applications”, Biochim Biophys Acta. 2003 Mar. 17; 1603(2):99-111.

The SNPs of the present invention also can be used to identify novel therapeutic targets for psoriasis. For example, genes containing the disease-associated variants (“variant genes”) or their products, as well as genes or their products that are directly or indirectly regulated by or interacting with these variant genes or their products, can be targeted for the development of therapeutics that, for example, treat the disease or prevent or delay disease onset. The therapeutics may be composed of, for example, small molecules, proteins, protein fragments or peptides, antibodies, nucleic acids, or their derivatives or mimetics which modulate the functions or levels of the target genes or gene products.

The SNP-containing nucleic acid molecules disclosed herein, and their complementary nucleic acid molecules, may be used as antisense constructs to control gene expression in cells, tissues, and organisms. Antisense technology is well established in the art and extensively reviewed in Antisense Drug Technology: Principles, Strategies, and Applications, Crooke (ed.), Marcel Dekker, Inc.: New York (2001). An antisense nucleic acid molecule is generally designed to be complementary to a region of mRNA expressed by a gene so that the antisense molecule hybridizes to the mRNA and thereby blocks translation of mRNA into protein. Various classes of antisense oligonucleotides are used in the art, two of which are cleavers and blockers. Cleavers, by binding to target RNAs, activate intracellular nucleases (e.g., RNaseH or RNase L) that cleave the target RNA. Blockers, which also bind to target RNAs, inhibit protein translation through steric hindrance of ribosomes. Exemplary blockers include peptide nucleic acids, morpholinos, locked nucleic acids, and methylphosphonates (see, e.g., Thompson, Drug Discovery Today, 7 (17): 912-917 (2002)). Antisense oligonucleotides are directly useful as therapeutic agents, and are also useful for determining and validating gene function (e.g., in gene knock-out or knock-down experiments).

Antisense technology is further reviewed in: Lavery et al., “Antisense and RNAi: powerful tools in drug target discovery and validation”, Curr Opin Drug Discov Devel. 2003 July; 6(4):561-9; Stephens et al., “Antisense oligonucleotide therapy in cancer”, Curr Opin Mol. Ther. 2003 April; 5(2): 118-22; Kurreck, “Antisense technologies. Improvement through novel chemical modifications”, Eur J. Biochem. 2003 April; 270(8):1628-44; Dias et al., “Antisense oligonucleotides: basic concepts and mechanisms”, Mol Cancer Ther. 2002 March; 1(5):347-55; Chen, “Clinical development of antisense oligonucleotides as anti-cancer therapeutics”, Methods Mol. Med. 2003; 75:621-46; Wang et al., “Antisense anticancer oligonucleotide therapeutics”, Curr Cancer Drug Targets. 2001 November; 1(3):177-96; and Bennett, “Efficiency of antisense oligonucleotide drug discovery”, Antisense Nucleic Acid Drug Dev. 2002 June; 12(3):215-24.

The SNPs of the present invention are particularly useful for designing antisense reagents that are specific for particular nucleic acid variants. Based on the SNP information disclosed herein, antisense oligonucleotides can be produced that specifically target mRNA molecules that contain one or more particular SNP nucleotides. In this manner, expression of mRNA molecules that contain one or more undesired polymorphisms (e.g., SNP nucleotides that lead to a defective protein such as an amino acid substitution in a catalytic domain) can be inhibited or completely blocked. Thus, antisense oligonucleotides can be used to specifically bind a particular polymorphic form (e.g., a SNP allele that encodes a defective protein), thereby inhibiting translation of this form, but which do not bind an alternative polymorphic form (e.g., an alternative SNP nucleotide that encodes a protein having normal function).

Antisense molecules can be used to inactivate mRNA in order to inhibit gene expression and production of defective proteins. Accordingly, these molecules can be used to treat a disorder, such as psoriasis, characterized by abnormal or undesired gene expression or expression of certain defective proteins. This technique can involve cleavage by means of ribozymes containing nucleotide sequences complementary to one or more regions in the mRNA that attenuate the ability of the mRNA to be translated. Possible mRNA regions include, for example, protein-coding regions and particularly protein-coding regions corresponding to catalytic activities, substrate/ligand binding, or other functional activities of a protein.

The SNPs of the present invention are also useful for designing RNA interference reagents that specifically target nucleic acid molecules having particular SNP variants. RNA interference (RNAi), also referred to as gene silencing, is based on using double-stranded RNA (dsRNA) molecules to turn genes off. When introduced into a cell, dsRNAs are processed by the cell into short fragments (generally about 21, 22, or 23 nucleotides in length) known as small interfering RNAs (siRNAs) which the cell uses in a sequence-specific manner to recognize and destroy complementary RNAs (Thompson, Drug Discovery Today, 7 (17): 912-917 (2002)). Accordingly, an aspect of the present invention specifically contemplates isolated nucleic acid molecules that are about 18-26 nucleotides in length, preferably 19-25 nucleotides in length, and more preferably 20, 21, 22, or 23 nucleotides in length, and the use of these nucleic acid molecules for RNAi. Because RNAi molecules, including siRNAs, act in a sequence-specific manner, the SNPs of the present invention can be used to design RNAi reagents that recognize and destroy nucleic acid molecules having specific SNP alleles/nucleotides (such as deleterious alleles that lead to the production of defective proteins), while not affecting nucleic acid molecules having alternative SNP alleles (such as alleles that encode proteins having normal function). As with antisense reagents, RNAi reagents may be directly useful as therapeutic agents (e.g., for turning off defective, disease-causing genes), and are also useful for characterizing and validating gene function (e.g., in gene knock-out or knock-down experiments).

The following references provide a further review of RNAi: Reynolds et al., “Rational siRNA design for RNA interference”, Nat. Biotechnol. 2004 March; 22(3):326-30. Epub 2004 Feb. 01; Chi et al., “Genomewide view of gene silencing by small interfering RNAs”, PNAS 100(11):6343-6346, 2003; Vickers et al., “Efficient Reduction of Target RNAs by Small Interfering RNA and RNase H-dependent Antisense Agents”, J. Biol. Chem. 278: 7108-7118, 2003; Agami, “RNAi and related mechanisms and their potential use for therapy”, Curr Opin Chem. Biol. 2002 December; 6(6):829-34; Layery et al., “Antisense and RNAi: powerful tools in drug target discovery and validation”, Curr Opin Drug Discov Devel. 2003 July; 6(4):561-9; Shi, “Mammalian RNAi for the masses”, Trends Genet. 2003 January; 19(1):9-12), Shuey et al., “RNAi: gene-silencing in therapeutic intervention”, Drug Discovery Today 2002 October; 7(20):1040-1046; McManus et al., Nat Rev Genet. 2002 October; 3(10):737-47; Xia et al., Nat Biotechnol 2002 October; 20(10): 1006-10; Plasterk et al., Curr Opin Genet Dev 2000 October; 10(5):562-7; Bosher et al., Nat Cell Biol 2000 February; 2(2):E31-6; and Hunter, Curr Biol 1999 Jun. 17; 9(12):R440-2).

A subject suffering from a pathological condition, such as psoriasis, ascribed to a SNP may be treated so as to correct the genetic defect (see Kren et al., Proc. Natl. Acad. Sci. USA 96:10349-10354 (1999)). Such a subject can be identified by any method that can detect the polymorphism in a biological sample drawn from the subject. Such a genetic defect may be permanently corrected by administering to such a subject a nucleic acid fragment incorporating a repair sequence that supplies the normal/wild-type nucleotide at the position of the SNP. This site-specific repair sequence can encompass an RNA/DNA oligonucleotide that operates to promote endogenous repair of a subject's genomic DNA. The site-specific repair sequence is administered in an appropriate vehicle, such as a complex with polyethylenimine, encapsulated in anionic liposomes, a viral vector such as an adenovirus, or other pharmaceutical composition that promotes intracellular uptake of the administered nucleic acid. A genetic defect leading to an inborn pathology may then be overcome, as the chimeric oligonucleotides induce incorporation of the normal sequence into the subject's genome. Upon incorporation, the normal gene product is expressed, and the replacement is propagated, thereby engendering a permanent repair and therapeutic enhancement of the clinical condition of the subject.

In cases in which a cSNP results in a variant protein that is ascribed to be the cause of, or a contributing factor to, a pathological condition, a method of treating such a condition can include administering to a subject experiencing the pathology the wild-type/normal cognate of the variant protein. Once administered in an effective dosing regimen, the wild-type cognate provides complementation or remediation of the pathological condition.

The invention further provides a method for identifying a compound or agent that can be used to treat psoriasis. The SNPs disclosed herein are useful as targets for the identification and/or development of therapeutic agents. A method for identifying a therapeutic agent or compound typically includes assaying the ability of the agent or compound to modulate the activity and/or expression of a SNP-containing nucleic acid or the encoded product and thus identifying an agent or a compound that can be used to treat a disorder characterized by undesired activity or expression of the SNP-containing nucleic acid or the encoded product. The assays can be performed in cell-based and cell-free systems. Cell-based assays can include cells naturally expressing the nucleic acid molecules of interest or recombinant cells genetically engineered to express certain nucleic acid molecules.

Variant gene expression in a psoriasis patient can include, for example, either expression of a SNP-containing nucleic acid sequence (for instance, a gene that contains a SNP can be transcribed into an mRNA transcript molecule containing the SNP, which can in turn be translated into a variant protein) or altered expression of a normal/wild-type nucleic acid sequence due to one or more SNPs (for instance, a regulatory/control region can contain a SNP that affects the level or pattern of expression of a normal transcript).

Assays for variant gene expression can involve direct assays of nucleic acid levels (e.g., mRNA levels), expressed protein levels, or of collateral compounds involved in a signal pathway. Further, the expression of genes that are up- or down-regulated in response to the signal pathway can also be assayed. In this embodiment, the regulatory regions of these genes can be operably linked to a reporter gene such as luciferase.

Modulators of variant gene expression can be identified in a method wherein, for example, a cell is contacted with a candidate compound/agent and the expression of mRNA determined. The level of expression of mRNA in the presence of the candidate compound is compared to the level of expression of mRNA in the absence of the candidate compound. The candidate compound can then be identified as a modulator of variant gene expression based on this comparison and be used to treat a disorder such as psoriasis that is characterized by variant gene expression (e.g., either expression of a SNP-containing nucleic acid or altered expression of a normal/wild-type nucleic acid molecule due to one or more SNPs that affect expression of the nucleic acid molecule) due to one or more SNPs of the present invention. When expression of mRNA is statistically significantly greater in the presence of the candidate compound than in its absence, the candidate compound is identified as a stimulator of nucleic acid expression. When nucleic acid expression is statistically significantly less in the presence of the candidate compound than in its absence, the candidate compound is identified as an inhibitor of nucleic acid expression.

The invention further provides methods of treatment, with the SNP or associated nucleic acid domain (e.g., catalytic domain, ligand/substrate-binding domain, regulatory/control region, etc.) or gene, or the encoded mRNA transcript, as a target, using a compound identified through drug screening as a gene modulator to modulate variant nucleic acid expression. Modulation can include either up-regulation (i.e., activation or agonization) or down-regulation (i.e., suppression or antagonization) of nucleic acid expression.

Expression of mRNA transcripts and encoded proteins, either wild type or variant, may be altered in individuals with a particular SNP allele in a regulatory/control element, such as a promoter or transcription factor binding domain, that regulates expression. In this situation, methods of treatment and compounds can be identified, as discussed herein, that regulate or overcome the variant regulatory/control element, thereby generating normal, or healthy, expression levels of either the wild type or variant protein.

The SNP-containing nucleic acid molecules of the present invention are also useful for monitoring the effectiveness of modulating compounds on the expression or activity of a variant gene, or encoded product, in clinical trials or in a treatment regimen. Thus, the gene expression pattern can serve as an indicator for the continuing effectiveness of treatment with the compound, particularly with compounds to which a patient can develop resistance, as well as an indicator for toxicities. The gene expression pattern can also serve as a marker indicative of a physiological response of the affected cells to the compound. Accordingly, such monitoring would allow either increased administration of the compound or the administration of alternative compounds to which the patient has not become resistant. Similarly, if the level of nucleic acid expression falls below a desirable level, administration of the compound could be commensurately decreased.

In another aspect of the present invention, there is provided a pharmaceutical pack comprising a therapeutic agent (e.g., a small molecule drug, antibody, peptide, antisense or RNAi nucleic acid molecule, etc.) and a set of instructions for administration of the therapeutic agent to humans diagnostically tested for one or more SNPs or SNP haplotypes provided by the present invention.

The SNPs/haplotypes of the present invention are also useful for improving many different aspects of the drug development process. For instance, an aspect of the present invention includes selecting individuals for clinical trials based on their SNP genotype. For example, individuals with SNP genotypes that indicate that they are likely to positively respond to a drug can be included in the trials, whereas those individuals whose SNP genotypes indicate that they are less likely to or would not respond to the drug, or who are at risk for suffering toxic effects or other adverse reactions, can be excluded from the clinical trials. This not only can improve the safety of clinical trials, but also can enhance the chances that the trial will demonstrate statistically significant efficacy. Furthermore, the SNPs of the present invention may explain why certain previously developed drugs performed poorly in clinical trials and may help identify a subset of the population that would benefit from a drug that had previously performed poorly in clinical trials, thereby “rescuing” previously developed drugs, and enabling the drug to be made available to a particular psoriasis patient population that can benefit from it.

SNPs have many important uses in drug discovery, screening, and development. A high probability exists that, for any gene/protein selected as a potential drug target, variants of that gene/protein will exist in a patient population. Thus, determining the impact of gene/protein variants on the selection and delivery of a therapeutic agent should be an integral aspect of the drug discovery and development process. (Jazwinska, A Trends Guide to Genetic Variation and Genomic Medicine, 2002 March; S30-S36).

Knowledge of variants (e.g., SNPs and any corresponding amino acid polymorphisms) of a particular therapeutic target (e.g., a gene, mRNA transcript, or protein) enables parallel screening of the variants in order to identify therapeutic candidates (e.g., small molecule compounds, antibodies, antisense or RNAi nucleic acid compounds, etc.) that demonstrate efficacy across variants (Rothberg, Nat Biotechnol 2001 March; 19(3):209-11). Such therapeutic candidates would be expected to show equal efficacy across a larger segment of the patient population, thereby leading to a larger potential market for the therapeutic candidate.

Furthermore, identifying variants of a potential therapeutic target enables the most common form of the target to be used for selection of therapeutic candidates, thereby helping to ensure that the experimental activity that is observed for the selected candidates reflects the real activity expected in the largest proportion of a patient population (Jazwinska, A Trends Guide to Genetic Variation and Genomic Medicine, 2002 March; S30-S36).

Additionally, screening therapeutic candidates against all known variants of a target can enable the early identification of potential toxicities and adverse reactions relating to particular variants. For example, variability in drug absorption, distribution, metabolism and excretion (ADME) caused by, for example, SNPs in therapeutic targets or drug metabolizing genes, can be identified, and this information can be utilized during the drug development process to minimize variability in drug disposition and develop therapeutic agents that are safer across a wider range of a patient population. The SNPs of the present invention, including the variant proteins and encoding polymorphic nucleic acid molecules provided in Tables 1-2, are useful in conjunction with a variety of toxicology methods established in the art, such as those set forth in Current Protocols in Toxicology, John Wiley & Sons, Inc., N.Y.

Furthermore, therapeutic agents that target any art-known proteins (or nucleic acid molecules, either RNA or DNA) may cross-react with the variant proteins (or polymorphic nucleic acid molecules) disclosed in Table 1, thereby significantly affecting the pharmacokinetic properties of the drug. Consequently, the protein variants and the SNP-containing nucleic acid molecules disclosed in Tables 1-2 are useful in developing, screening, and evaluating therapeutic agents that target corresponding art-known protein forms (or nucleic acid molecules). Additionally, as discussed above, knowledge of all polymorphic forms of a particular drug target enables the design of therapeutic agents that are effective against most or all such polymorphic forms of the drug target.

Pharmaceutical Compositions and Administration Thereof

Any of the psoriasis-associated proteins, and encoding nucleic acid molecules, disclosed herein can be used as therapeutic targets (or directly used themselves as therapeutic compounds) for treating psoriasis and related pathologies, and the present disclosure enables therapeutic compounds (e.g., small molecules, antibodies, therapeutic proteins, RNAi and antisense molecules, etc.) to be developed that target (or are comprised of) any of these therapeutic targets.

In general, a therapeutic compound will be administered in a therapeutically effective amount by any of the accepted modes of administration for agents that serve similar utilities. The actual amount of the therapeutic compound of this invention, i.e., the active ingredient, will depend upon numerous factors such as the severity of the disease to be treated, the age and relative health of the subject, the potency of the compound used, the route and form of administration, and other factors.

Therapeutically effective amounts of therapeutic compounds may range from, for example, approximately 0.01-50 mg per kilogram body weight of the recipient per day; preferably about 0.1-20 mg/kg/day. Thus, as an example, for administration to a 70 kg person, the dosage range would most preferably be about 7 mg to 1.4 g per day.

In general, therapeutic compounds will be administered as pharmaceutical compositions by any one of the following routes: oral, systemic (e.g., transdermal, intranasal, or by suppository), or parenteral (e.g., intramuscular, intravenous, or subcutaneous) administration. The preferred manner of administration is oral or parenteral using a convenient daily dosage regimen, which can be adjusted according to the degree of affliction. Oral compositions can take the form of tablets, pills, capsules, semisolids, powders, sustained release formulations, solutions, suspensions, elixirs, aerosols, or any other appropriate compositions.

The choice of formulation depends on various factors such as the mode of drug administration (e.g., for oral administration, formulations in the form of tablets, pills, or capsules are preferred) and the bioavailability of the drug substance. Recently, pharmaceutical formulations have been developed especially for drugs that show poor bioavailability based upon the principle that bioavailability can be increased by increasing the surface area, i.e., decreasing particle size. For example, U.S. Pat. No. 4,107,288 describes a pharmaceutical formulation having particles in the size range from 10 to 1,000 nm in which the active material is supported on a cross-linked matrix of macromolecules. U.S. Pat. No. 5,145,684 describes the production of a pharmaceutical formulation in which the drug substance is pulverized to nanoparticles (average particle size of 400 nm) in the presence of a surface modifier and then dispersed in a liquid medium to give a pharmaceutical formulation that exhibits remarkably high bioavailability.

Pharmaceutical compositions are comprised of, in general, a therapeutic compound in combination with at least one pharmaceutically acceptable excipient. Acceptable excipients are non-toxic, aid administration, and do not adversely affect the therapeutic benefit of the therapeutic compound. Such excipients may be any solid, liquid, semi-solid or, in the case of an aerosol composition, gaseous excipient that is generally available to one skilled in the art.

Solid pharmaceutical excipients include starch, cellulose, talc, glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk, silica gel, magnesium stearate, sodium stearate, glycerol monostearate, sodium chloride, dried skim milk and the like. Liquid and semisolid excipients may be selected from glycerol, propylene glycol, water, ethanol and various oils, including those of petroleum, animal, vegetable or synthetic origin, e.g., peanut oil, soybean oil, mineral oil, sesame oil, etc. Preferred liquid carriers, particularly for injectable solutions, include water, saline, aqueous dextrose, and glycols.

Compressed gases may be used to disperse a compound of this invention in aerosol form. Inert gases suitable for this purpose are nitrogen, carbon dioxide, etc.

Other suitable pharmaceutical excipients and their formulations are described in Remington's Pharmaceutical Sciences, edited by E. W. Martin (Mack Publishing Company, 18^(th) ed., 1990).

The amount of the therapeutic compound in a formulation can vary within the full range employed by those skilled in the art. Typically, the formulation will contain, on a weight percent (wt %) basis, from about 0.01-99.99 wt % of the therapeutic compound based on the total formulation, with the balance being one or more suitable pharmaceutical excipients. Preferably, the compound is present at a level of about 1-80 wt %.

Therapeutic compounds can be administered alone or in combination with other therapeutic compounds or in combination with one or more other active ingredient(s). For example, an inhibitor or stimulator of a psoriasis-associated protein can be administered in combination with another agent that inhibits or stimulates the activity of the same or a different psoriasis-associated protein to thereby counteract the affects of psoriasis.

For further information regarding pharmacology, see Current Protocols in Pharmacology, John Wiley & Sons, Inc., N.Y.

Human Identification Applications

In addition to their diagnostic and therapeutic uses in psoriasis and related pathologies, the SNPs provided by the present invention are also useful as human identification markers for such applications as forensics, paternity testing, and biometrics (see, e.g., Gill, “An assessment of the utility of single nucleotide polymorphisms (SNPs) for forensic purposes”, Int J Legal Med. 2001; 114(4-5):204-10). Genetic variations in the nucleic acid sequences between individuals can be used as genetic markers to identify individuals and to associate a biological sample with an individual. Determination of which nucleotides occupy a set of SNP positions in an individual identifies a set of SNP markers that distinguishes the individual. The more SNP positions that are analyzed, the lower the probability that the set of SNPs in one individual is the same as that in an unrelated individual. Preferably, if multiple sites are analyzed, the sites are unlinked (i.e., inherited independently). Thus, preferred sets of SNPs can be selected from among the SNPs disclosed herein, which may include SNPs on different chromosomes, SNPs on different chromosome arms, and/or SNPs that are dispersed over substantial distances along the same chromosome arm.

Furthermore, among the SNPs disclosed herein, preferred SNPs for use in certain forensic/human identification applications include SNPs located at degenerate codon positions (i.e., the third position in certain codons which can be one of two or more alternative nucleotides and still encode the same amino acid), since these SNPs do not affect the encoded protein. SNPs that do not affect the encoded protein are expected to be under less selective pressure and are therefore expected to be more polymorphic in a population, which is typically an advantage for forensic/human identification applications. However, for certain forensics/human identification applications, such as predicting phenotypic characteristics (e.g., inferring ancestry or inferring one or more physical characteristics of an individual) from a DNA sample, it may be desirable to utilize SNPs that affect the encoded protein.

For many of the SNPs disclosed in Tables 1-2 (which are identified as “Applera” SNP source), Tables 1-2 provide SNP allele frequencies obtained by re-sequencing the DNA of chromosomes from 39 individuals (Tables 1-2 also provide allele frequency information for “Celera” source SNPs and, where available, public SNPs from dbEST, HGBASE, and/or HGMD). The allele frequencies provided in Tables 1-2 enable these SNPs to be readily used for human identification applications. Although any SNP disclosed in Table 1 and/or Table 2 could be used for human identification, the closer that the frequency of the minor allele at a particular SNP site is to 50%, the greater the ability of that SNP to discriminate between different individuals in a population since it becomes increasingly likely that two randomly selected individuals would have different alleles at that SNP site. Using the SNP allele frequencies provided in Tables 1-2, one of ordinary skill in the art could readily select a subset of SNPs for which the frequency of the minor allele is, for example, at least 1%, 2%, 5%, 10%, 20%, 25%, 30%, 40%, 45%, or 50%, or any other frequency in-between. Thus, since Tables 1-2 provide allele frequencies based on the re-sequencing of the chromosomes from 39 individuals, a subset of SNPs could readily be selected for human identification in which the total allele count of the minor allele at a particular SNP site is, for example, at least 1, 2, 4, 8, 10, 16, 20, 24, 30, 32, 36, 38, 39, 40, or any other number in-between.

Furthermore, Tables 1-2 also provide population group (interchangeably referred to herein as ethnic or racial groups) information coupled with the extensive allele frequency information. For example, the group of 39 individuals whose DNA was re-sequenced was made-up of 20 Caucasians and 19 African-Americans. This population group information enables further refinement of SNP selection for human identification. For example, preferred SNPs for human identification can be selected from Tables 1-2 that have similar allele frequencies in both the Caucasian and African-American populations; thus, for example, SNPs can be selected that have equally high discriminatory power in both populations. Alternatively, SNPs can be selected for which there is a statistically significant difference in allele frequencies between the Caucasian and African-American populations (as an extreme example, a particular allele may be observed only in either the Caucasian or the African-American population group but not observed in the other population group); such SNPs are useful, for example, for predicting the race/ethnicity of an unknown perpetrator from a biological sample such as a hair or blood stain recovered at a crime scene. For a discussion of using SNPs to predict ancestry from a DNA sample, including statistical methods, see Frudakis et al., “A Classifier for the SNP-Based Inference of Ancestry”, Journal of Forensic Sciences 2003; 48(4):771-782.

SNPs have numerous advantages over other types of polymorphic markers, such as short tandem repeats (STRs). For example, SNPs can be easily scored and are amenable to automation, making SNPs the markers of choice for large-scale forensic databases. SNPs are found in much greater abundance throughout the genome than repeat polymorphisms. Population frequencies of two polymorphic forms can usually be determined with greater accuracy than those of multiple polymorphic forms at multi-allelic loci. SNPs are mutationaly more stable than repeat polymorphisms. SNPs are not susceptible to artifacts such as stutter bands that can hinder analysis. Stutter bands are frequently encountered when analyzing repeat polymorphisms, and are particularly troublesome when analyzing samples such as crime scene samples that may contain mixtures of DNA from multiple sources. Another significant advantage of SNP markers over STR markers is the much shorter length of nucleic acid needed to score a SNP. For example, STR markers are generally several hundred base pairs in length. A SNP, on the other hand, comprises a single nucleotide, and generally a short conserved region on either side of the SNP position for primer and/or probe binding. This makes SNPs more amenable to typing in highly degraded or aged biological samples that are frequently encountered in forensic casework in which DNA may be fragmented into short pieces.

SNPs also are not subject to microvariant and “off-ladder” alleles frequently encountered when analyzing STR loci. Microvariants are deletions or insertions within a repeat unit that change the size of the amplified DNA product so that the amplified product does not migrate at the same rate as reference alleles with normal sized repeat units. When separated by size, such as by electrophoresis on a polyacrylamide gel, microvariants do not align with a reference allelic ladder of standard sized repeat units, but rather migrate between the reference alleles. The reference allelic ladder is used for precise sizing of alleles for allele classification; therefore alleles that do not align with the reference allelic ladder lead to substantial analysis problems. Furthermore, when analyzing multi-allelic repeat polymorphisms, occasionally an allele is found that consists of more or less repeat units than has been previously seen in the population, or more or less repeat alleles than are included in a reference allelic ladder. These alleles will migrate outside the size range of known alleles in a reference allelic ladder, and therefore are referred to as “off-ladder” alleles. In extreme cases, the allele may contain so few or so many repeats that it migrates well out of the range of the reference allelic ladder. In this situation, the allele may not even be observed, or, with multiplex analysis, it may migrate within or close to the size range for another locus, further confounding analysis.

SNP analysis avoids the problems of microvariants and off-ladder alleles encountered in STR analysis. Importantly, microvariants and off-ladder alleles may provide significant problems, and may be completely missed, when using analysis methods such as oligonucleotide hybridization arrays, which utilize oligonucleotide probes specific for certain known alleles. Furthermore, off-ladder alleles and microvariants encountered with STR analysis, even when correctly typed, may lead to improper statistical analysis, since their frequencies in the population are generally unknown or poorly characterized, and therefore the statistical significance of a matching genotype may be questionable. All these advantages of SNP analysis are considerable in light of the consequences of most DNA identification cases, which may lead to life imprisonment for an individual, or re-association of remains to the family of a deceased individual.

DNA can be isolated from biological samples such as blood, bone, hair, saliva, or semen, and compared with the DNA from a reference source at particular SNP positions. Multiple SNP markers can be assayed simultaneously in order to increase the power of discrimination and the statistical significance of a matching genotype. For example, oligonucleotide arrays can be used to genotype a large number of SNPs simultaneously. The SNPs provided by the present invention can be assayed in combination with other polymorphic genetic markers, such as other SNPs known in the art or STRs, in order to identify an individual or to associate an individual with a particular biological sample.

Furthermore, the SNPs provided by the present invention can be genotyped for inclusion in a database of DNA genotypes, for example, a criminal DNA databank such as the FBI's Combined DNA Index System (CODIS) database. A genotype obtained from a biological sample of unknown source can then be queried against the database to find a matching genotype, with the SNPs of the present invention providing nucleotide positions at which to compare the known and unknown DNA sequences for identity. Accordingly, the present invention provides a database comprising novel SNPs or SNP alleles of the present invention (e.g., the database can comprise information indicating which alleles are possessed by individual members of a population at one or more novel SNP sites of the present invention), such as for use in forensics, biometrics, or other human identification applications. Such a database typically comprises a computer-based system in which the SNPs or SNP alleles of the present invention are recorded on a computer readable medium (see the section of the present specification entitled “Computer-Related Embodiments”).

The SNPs of the present invention can also be assayed for use in paternity testing. The object of paternity testing is usually to determine whether a male is the father of a child. In most cases, the mother of the child is known and thus, the mother's contribution to the child's genotype can be traced. Paternity testing investigates whether the part of the child's genotype not attributable to the mother is consistent with that of the putative father. Paternity testing can be performed by analyzing sets of polymorphisms in the putative father and the child, with the SNPs of the present invention providing nucleotide positions at which to compare the putative father's and child's DNA sequences for identity. If the set of polymorphisms in the child attributable to the father does not match the set of polymorphisms of the putative father, it can be concluded, barring experimental error, that the putative father is not the father of the child. If the set of polymorphisms in the child attributable to the father match the set of polymorphisms of the putative father, a statistical calculation can be performed to determine the probability of coincidental match, and a conclusion drawn as to the likelihood that the putative father is the true biological father of the child.

In addition to paternity testing, SNPs are also useful for other types of kinship testing, such as for verifying familial relationships for immigration purposes, or for cases in which an individual alleges to be related to a deceased individual in order to claim an inheritance from the deceased individual, etc. For further information regarding the utility of SNPs for paternity testing and other types of kinship testing, including methods for statistical analysis, see Krawczak, “Informativity assessment for biallelic single nucleotide polymorphisms”, Electrophoresis 1999 June; 20(8):1676-81.

The use of the SNPs of the present invention for human identification further extends to various authentication systems, commonly referred to as biometric systems, which typically convert physical characteristics of humans (or other organisms) into digital data. Biometric systems include various technological devices that measure such unique anatomical or physiological characteristics as finger, thumb, or palm prints; hand geometry; vein patterning on the back of the hand; blood vessel patterning of the retina and color and texture of the iris; facial characteristics; voice patterns; signature and typing dynamics; and DNA. Such physiological measurements can be used to verify identity and, for example, restrict or allow access based on the identification. Examples of applications for biometrics include physical area security, computer and network security, aircraft passenger check-in and boarding, financial transactions, medical records access, government benefit distribution, voting, law enforcement, passports, visas and immigration, prisons, various military applications, and for restricting access to expensive or dangerous items, such as automobiles or guns (see, for example, O'Connor, Stanford Technology Law Review and U.S. Pat. No. 6,119,096).

Groups of SNPs, particularly the SNPs provided by the present invention, can be typed to uniquely identify an individual for biometric applications such as those described above. Such SNP typing can readily be accomplished using, for example, DNA chips/arrays. Preferably, a minimally invasive means for obtaining a DNA sample is utilized. For example, PCR amplification enables sufficient quantities of DNA for analysis to be obtained from buccal swabs or fingerprints, which contain DNA-containing skin cells and oils that are naturally transferred during contact. Further information regarding techniques for using SNPs in forensic/human identification applications can be found in, for example, Current Protocols in Human Genetics, John Wiley & Sons, N.Y. (2002), 14.1-14.7.

Variant Proteins, Antibodies, Vectors & Host Cells, & Uses Thereof

Variant Proteins Encoded by SNP-Containing Nucleic Acid Molecules

The present invention provides SNP-containing nucleic acid molecules, many of which encode proteins having variant amino acid sequences as compared to the art-known (i.e., wild-type) proteins. Amino acid sequences encoded by the polymorphic nucleic acid molecules of the present invention are provided as SEQ ID NOS:3-4 in Table 1 and the Sequence Listing. These variants will generally be referred to herein as variant proteins/peptides/polypeptides, or polymorphic proteins/peptides/polypeptides of the present invention. The terms “protein”, “peptide”, and “polypeptide” are used herein interchangeably.

A variant protein of the present invention may be encoded by, for example, a nonsynonymous nucleotide substitution at any one of the cSNP positions disclosed herein. In addition, variant proteins may also include proteins whose expression, structure, and/or function is altered by a SNP disclosed herein, such as a SNP that creates or destroys a stop codon, a SNP that affects splicing, and a SNP in control/regulatory elements, e.g. promoters, enhancers, or transcription factor binding domains.

As used herein, a protein or peptide is said to be “isolated” or “purified” when it is substantially free of cellular material or chemical precursors or other chemicals. The variant proteins of the present invention can be purified to homogeneity or other lower degrees of purity. The level of purification will be based on the intended use. The key feature is that the preparation allows for the desired function of the variant protein, even if in the presence of considerable amounts of other components.

As used herein, “substantially free of cellular material” includes preparations of the variant protein having less than about 30% (by dry weight) other proteins (i.e., contaminating protein), less than about 20% other proteins, less than about 10% other proteins, or less than about 5% other proteins. When the variant protein is recombinantly produced, it can also be substantially free of culture medium, i.e., culture medium represents less than about 20% of the volume of the protein preparation.

The language “substantially free of chemical precursors or other chemicals” includes preparations of the variant protein in which it is separated from chemical precursors or other chemicals that are involved in its synthesis. In one embodiment, the language “substantially free of chemical precursors or other chemicals” includes preparations of the variant protein having less than about 30% (by dry weight) chemical precursors or other chemicals, less than about 20% chemical precursors or other chemicals, less than about 10% chemical precursors or other chemicals, or less than about 5% chemical precursors or other chemicals.

An isolated variant protein may be purified from cells that naturally express it, purified from cells that have been altered to express it (recombinant host cells), or synthesized using known protein synthesis methods. For example, a nucleic acid molecule containing SNP(s) encoding the variant protein can be cloned into an expression vector, the expression vector introduced into a host cell, and the variant protein expressed in the host cell. The variant protein can then be isolated from the cells by any appropriate purification scheme using standard protein purification techniques. Examples of these techniques are described in detail below (Sambrook and Russell, 2000, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.).

The present invention provides isolated variant proteins that comprise, consist of or consist essentially of amino acid sequences that contain one or more variant amino acids encoded by one or more codons which contain a SNP of the present invention.

Accordingly, the present invention provides variant proteins that consist of amino acid sequences that contain one or more amino acid polymorphisms (or truncations or extensions due to creation or destruction of a stop codon, respectively) encoded by the SNPs provided in Table 1 and/or Table 2. A protein consists of an amino acid sequence when the amino acid sequence is the entire amino acid sequence of the protein.

The present invention further provides variant proteins that consist essentially of amino acid sequences that contain one or more amino acid polymorphisms (or truncations or extensions due to creation or destruction of a stop codon, respectively) encoded by the SNPs provided in Table 1 and/or Table 2. A protein consists essentially of an amino acid sequence when such an amino acid sequence is present with only a few additional amino acid residues in the final protein.

The present invention further provides variant proteins that comprise amino acid sequences that contain one or more amino acid polymorphisms (or truncations or extensions due to creation or destruction of a stop codon, respectively) encoded by the SNPs provided in Table 1 and/or Table 2. A protein comprises an amino acid sequence when the amino acid sequence is at least part of the final amino acid sequence of the protein. In such a fashion, the protein may contain only the variant amino acid sequence or have additional amino acid residues, such as a contiguous encoded sequence that is naturally associated with it or heterologous amino acid residues. Such a protein can have a few additional amino acid residues or can comprise many more additional amino acids. A brief description of how various types of these proteins can be made and isolated is provided below.

The variant proteins of the present invention can be attached to heterologous sequences to form chimeric or fusion proteins. Such chimeric and fusion proteins comprise a variant protein operatively linked to a heterologous protein having an amino acid sequence not substantially homologous to the variant protein. “Operatively linked” indicates that the coding sequences for the variant protein and the heterologous protein are ligated in-frame. The heterologous protein can be fused to the N-terminus or C-terminus of the variant protein. In another embodiment, the fusion protein is encoded by a fusion polynucleotide that is synthesized by conventional techniques including automated DNA synthesizers. Alternatively, PCR amplification of gene fragments can be carried out using anchor primers which give rise to complementary overhangs between two consecutive gene fragments which can subsequently be annealed and re-amplified to generate a chimeric gene sequence (see Ausubel et al., Current Protocols in Molecular Biology, 1992). Moreover, many expression vectors are commercially available that already encode a fusion moiety (e.g., a GST protein). A variant protein-encoding nucleic acid can be cloned into such an expression vector such that the fusion moiety is linked in-frame to the variant protein.

In many uses, the fusion protein does not affect the activity of the variant protein. The fusion protein can include, but is not limited to, enzymatic fusion proteins, for example, beta-galactosidase fusions, yeast two-hybrid GAL fusions, poly-His fusions, MYC-tagged, HI-tagged and Ig fusions. Such fusion proteins, particularly poly-His fusions, can facilitate their purification following recombinant expression. In certain host cells (e.g., mammalian host cells), expression and/or secretion of a protein can be increased by using a heterologous signal sequence. Fusion proteins are further described in, for example, Terpe, “Overview of tag protein fusions: from molecular and biochemical fundamentals to commercial systems”, Appl Microbiol Biotechnol. 2003 January; 60(5):523-33. Epub 2002 Nov. 07; Graddis et al., “Designing proteins that work using recombinant technologies”, Curr Pharm Biotechnol. 2002 December; 3(4):285-97; and Nilsson et al., “Affinity fusion strategies for detection, purification, and immobilization of recombinant proteins”, Protein Expr Purif. 1997 October; 11 (1): 1-16.

The present invention also relates to further obvious variants of the variant polypeptides of the present invention, such as naturally-occurring mature forms (e.g., alleleic variants), non-naturally occurring recombinantly-derived variants, and orthologs and paralogs of such proteins that share sequence homology. Such variants can readily be generated using art-known techniques in the fields of recombinant nucleic acid technology and protein biochemistry. It is understood, however, that variants exclude those known in the prior art before the present invention.

Further variants of the variant polypeptides disclosed in Table 1 can comprise an amino acid sequence that shares at least 70-80%, 80-85%, 85-90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity with an amino acid sequence disclosed in Table 1 (or a fragment thereof) and that includes a novel amino acid residue (allele) disclosed in Table 1 (which is encoded by a novel SNP allele). Thus, an aspect of the present invention that is specifically contemplated are polypeptides that have a certain degree of sequence variation compared with the polypeptide sequences shown in Table 1, but that contain a novel amino acid residue (allele) encoded by a novel SNP allele disclosed herein. In other words, as long as a polypeptide contains a novel amino acid residue disclosed herein, other portions of the polypeptide that flank the novel amino acid residue can vary to some degree from the polypeptide sequences shown in Table 1.

Full-length pre-processed forms, as well as mature processed forms, of proteins that comprise one of the amino acid sequences disclosed herein can readily be identified as having complete sequence identity to one of the variant proteins of the present invention as well as being encoded by the same genetic locus as the variant proteins provided herein.

Orthologs of a variant peptide can readily be identified as having some degree of significant sequence homology/identity to at least a portion of a variant peptide as well as being encoded by a gene from another organism. Preferred orthologs will be isolated from non-human mammals, preferably primates, for the development of human therapeutic targets and agents. Such orthologs can be encoded by a nucleic acid sequence that hybridizes to a variant peptide-encoding nucleic acid molecule under moderate to stringent conditions depending on the degree of relatedness of the two organisms yielding the homologous proteins.

Variant proteins include, but are not limited to, proteins containing deletions, additions and substitutions in the amino acid sequence caused by the SNPs of the present invention. One class of substitutions is conserved amino acid substitutions in which a given amino acid in a polypeptide is substituted for another amino acid of like characteristics. Typical conservative substitutions are replacements, one for another, among the aliphatic amino acids Ala, Val, Leu, and Ile; interchange of the hydroxyl residues Ser and Thr; exchange of the acidic residues Asp and Glu; substitution between the amide residues Asn and Gln; exchange of the basic residues Lys and Arg; and replacements among the aromatic residues Phe and Tyr. Guidance concerning which amino acid changes are likely to be phenotypically silent are found in, for example, Bowie et al., Science 247:1306-1310 (1990).

Variant proteins can be fully functional or can lack function in one or more activities, e.g. ability to bind another molecule, ability to catalyze a substrate, ability to mediate signaling, etc. Fully functional variants typically contain only conservative variations or variations in non-critical residues or in non-critical regions. Functional variants can also contain substitution of similar amino acids that result in no change or an insignificant change in function. Alternatively, such substitutions may positively or negatively affect function to some degree. Non-functional variants typically contain one or more non-conservative amino acid substitutions, deletions, insertions, inversions, truncations or extensions, or a substitution, insertion, inversion, or deletion of a critical residue or in a critical region.

Amino acids that are essential for function of a protein can be identified by methods known in the art, such as site-directed mutagenesis or alanine-scanning mutagenesis (Cunningham et al., Science 244:1081-1085 (1989)), particularly using the amino acid sequence and polymorphism information provided in Table 1. The latter procedure introduces single alanine mutations at every residue in the molecule. The resulting mutant molecules are then tested for biological activity such as enzyme activity or in assays such as an in vitro proliferative activity. Sites that are critical for binding partner/substrate binding can also be determined by structural analysis such as crystallization, nuclear magnetic resonance or photoaffinity labeling (Smith et al., J. Mol. Biol. 224:899-904 (1992); de Vos et al. Science 255:306-312 (1992)).

Polypeptides can contain amino acids other than the 20 amino acids commonly referred to as the 20 naturally occurring amino acids. Further, many amino acids, including the terminal amino acids, may be modified by natural processes, such as processing and other post-translational modifications, or by chemical modification techniques well known in the art. Accordingly, the variant proteins of the present invention also encompass derivatives or analogs in which a substituted amino acid residue is not one encoded by the genetic code, in which a substituent group is included, in which the mature polypeptide is fused with another compound, such as a compound to increase the half-life of the polypeptide (e.g., polyethylene glycol), or in which additional amino acids are fused to the mature polypeptide, such as a leader or secretory sequence or a sequence for purification of the mature polypeptide or a pro-protein sequence.

Known protein modifications include, but are not limited to, acetylation, acylation, ADP-ribosylation, amidation, covalent attachment of flavin, covalent attachment of a heme moiety, covalent attachment of a nucleotide or nucleotide derivative, covalent attachment of a lipid or lipid derivative, covalent attachment of phosphotidylinositol, cross-linking, cyclization, disulfide bond formation, demethylation, formation of covalent crosslinks, formation of cystine, formation of pyroglutamate, formylation, gamma carboxylation, glycosylation, GPI anchor formation, hydroxylation, iodination, methylation, myristoylation, oxidation, proteolytic processing, phosphorylation, prenylation, racemization, selenoylation, sulfation, transfer-RNA mediated addition of amino acids to proteins such as arginylation, and ubiquitination.

Such protein modifications are well known to those of skill in the art and have been described in great detail in the scientific literature. Several particularly common modifications, glycosylation, lipid attachment, sulfation, gamma-carboxylation of glutamic acid residues, hydroxylation and ADP-ribosylation, for instance, are described in most basic texts, such as Proteins—Structure and Molecular Properties, 2nd Ed., T. E. Creighton, W. H. Freeman and Company, New York (1993); Wold, F., Posttranslational Covalent Modification of Proteins, B. C. Johnson, Ed., Academic Press, New York 1-12 (1983); Seifter et al., Meth. Enzymol. 182: 626-646 (1990); and Rattan et al., Ann. N.Y. Acad. Sci. 663:48-62 (1992).

The present invention further provides fragments of the variant proteins in which the fragments contain one or more amino acid sequence variations (e.g., substitutions, or truncations or extensions due to creation or destruction of a stop codon) encoded by one or more SNPs disclosed herein. The fragments to which the invention pertains, however, are not to be construed as encompassing fragments that have been disclosed in the prior art before the present invention.

As used herein, a fragment may comprise at least about 4, 8, 10, 12, 14, 16, 18, 20, 25, 30, 50, 100 (or any other number in-between) or more contiguous amino acid residues from a variant protein, wherein at least one amino acid residue is affected by a SNP of the present invention, e.g., a variant amino acid residue encoded by a nonsynonymous nucleotide substitution at a cSNP position provided by the present invention. The variant amino acid encoded by a cSNP may occupy any residue position along the sequence of the fragment. Such fragments can be chosen based on the ability to retain one or more of the biological activities of the variant protein or the ability to perform a function, e.g., act as an immunogen. Particularly important fragments are biologically active fragments. Such fragments will typically comprise a domain or motif of a variant protein of the present invention, e.g., active site, transmembrane domain, or ligand/substrate binding domain. Other fragments include, but are not limited to, domain or motif-containing fragments, soluble peptide fragments, and fragments containing immunogenic structures. Predicted domains and functional sites are readily identifiable by computer programs well known to those of skill in the art (e.g., PROSITE analysis) (Current Protocols in Protein Science, John Wiley & Sons, N.Y. (2002)).

Uses of Variant Proteins

The variant proteins of the present invention can be used in a variety of ways, including but not limited to, in assays to determine the biological activity of a variant protein, such as in a panel of multiple proteins for high-throughput screening; to raise antibodies or to elicit another type of immune response; as a reagent (including the labeled reagent) in assays designed to quantitatively determine levels of the variant protein (or its binding partner) in biological fluids; as a marker for cells or tissues in which it is preferentially expressed (either constitutively or at a particular stage of tissue differentiation or development or in a disease state); as a target for screening for a therapeutic agent; and as a direct therapeutic agent to be administered into a human subject. Any of the variant proteins disclosed herein may be developed into reagent grade or kit format for commercialization as research products. Methods for performing the uses listed above are well known to those skilled in the art (see, e.g., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Sambrook and Russell, 2000, and Methods in Enzymology: Guide to Molecular Cloning Techniques, Academic Press, Berger, S. L. and A. R. Kimmel eds., 1987).

In a specific embodiment of the invention, the methods of the present invention include detection of one or more variant proteins disclosed herein. Variant proteins are disclosed in Table 1 and in the Sequence Listing as SEQ ID NOS:3-4. Detection of such proteins can be accomplished using, for example, antibodies, small molecule compounds, aptamers, ligands/substrates, other proteins or protein fragments, or other protein-binding agents. Preferably, protein detection agents are specific for a variant protein of the present invention and can therefore discriminate between a variant protein of the present invention and the wild-type protein or another variant form. This can generally be accomplished by, for example, selecting or designing detection agents that bind to the region of a protein that differs between the variant and wild-type protein, such as a region of a protein that contains one or more amino acid substitutions that is/are encoded by a non-synonymous cSNP of the present invention, or a region of a protein that follows a nonsense mutation-type SNP that creates a stop codon thereby leading to a shorter polypeptide, or a region of a protein that follows a read-through mutation-type SNP that destroys a stop codon thereby leading to a longer polypeptide in which a portion of the polypeptide is present in one version of the polypeptide but not the other.

In another specific aspect of the invention, the variant proteins of the present invention are used as targets for diagnosing psoriasis or for determining predisposition to psoriasis in a human. Accordingly, the invention provides methods for detecting the presence of, or levels of, one or more variant proteins of the present invention in a cell, tissue, or organism. Such methods typically involve contacting a test sample with an agent (e.g., an antibody, small molecule compound, or peptide) capable of interacting with the variant protein such that specific binding of the agent to the variant protein can be detected. Such an assay can be provided in a single detection format or a multi-detection format such as an array, for example, an antibody or aptamer array (arrays for protein detection may also be referred to as “protein chips”). The variant protein of interest can be isolated from a test sample and assayed for the presence of a variant amino acid sequence encoded by one or more SNPs disclosed by the present invention. The SNPs may cause changes to the protein and the corresponding protein function/activity, such as through non-synonymous substitutions in protein coding regions that can lead to amino acid substitutions, deletions, insertions, and/or rearrangements; formation or destruction of stop codons; or alteration of control elements such as promoters. SNPs may also cause inappropriate post-translational modifications.

One preferred agent for detecting a variant protein in a sample is an antibody capable of selectively binding to a variant form of the protein (antibodies are described in greater detail in the next section). Such samples include, for example, tissues, cells, and biological fluids isolated from a subject, as well as tissues, cells and fluids present within a subject.

In vitro methods for detection of the variant proteins associated with psoriasis that are disclosed herein and fragments thereof include, but are not limited to, enzyme linked immunosorbent assays (ELISAs), radioimmunoassays (RIA), Western blots, immunoprecipitations, immunofluorescence, and protein arrays/chips (e.g., arrays of antibodies or aptamers). For further information regarding immunoassays and related protein detection methods, see Current Protocols in Immunology, John Wiley & Sons, N.Y., and Hage, “Immunoassays”, Anal Chem. 1999 Jun. 15; 71(12):294R-304R.

Additional analytic methods of detecting amino acid variants include, but are not limited to, altered electrophoretic mobility, altered tryptic peptide digest, altered protein activity in cell-based or cell-free assay, alteration in ligand or antibody-binding pattern, altered isoelectric point, and direct amino acid sequencing.

Alternatively, variant proteins can be detected in vivo in a subject by introducing into the subject a labeled antibody (or other type of detection reagent) specific for a variant protein. For example, the antibody can be labeled with a radioactive marker whose presence and location in a subject can be detected by standard imaging techniques.

Other uses of the variant peptides of the present invention are based on the class or action of the protein. For example, proteins isolated from humans and their mammalian orthologs serve as targets for identifying agents (e.g., small molecule drugs or antibodies) for use in therapeutic applications, particularly for modulating a biological or pathological response in a cell or tissue that expresses the protein. Pharmaceutical agents can be developed that modulate protein activity.

As an alternative to modulating gene expression, therapeutic compounds can be developed that modulate protein function. For example, many SNPs disclosed herein affect the amino acid sequence of the encoded protein (e.g., non-synonymous cSNPs and nonsense mutation-type SNPs). Such alterations in the encoded amino acid sequence may affect protein function, particularly if such amino acid sequence variations occur in functional protein domains, such as catalytic domains, ATP-binding domains, or ligand/substrate binding domains. It is well established in the art that variant proteins having amino acid sequence variations in functional domains can cause or influence pathological conditions. In such instances, compounds (e.g., small molecule drugs or antibodies) can be developed that target the variant protein and modulate (e.g., up- or down-regulate) protein function/activity.

The therapeutic methods of the present invention further include methods that target one or more variant proteins of the present invention. Variant proteins can be targeted using, for example, small molecule compounds, antibodies, aptamers, ligands/substrates, other proteins, or other protein-binding agents. Additionally, the skilled artisan will recognize that the novel protein variants (and polymorphic nucleic acid molecules) disclosed in Table 1 may themselves be directly used as therapeutic agents by acting as competitive inhibitors of corresponding art-known proteins (or nucleic acid molecules such as mRNA molecules).

The variant proteins of the present invention are particularly useful in drug screening assays, in cell-based or cell-free systems. Cell-based systems can utilize cells that naturally express the protein, a biopsy specimen, or cell cultures. In one embodiment, cell-based assays involve recombinant host cells expressing the variant protein. Cell-free assays can be used to detect the ability of a compound to directly bind to a variant protein or to the corresponding SNP-containing nucleic acid fragment that encodes the variant protein.

A variant protein of the present invention, as well as appropriate fragments thereof, can be used in high-throughput screening assays to test candidate compounds for the ability to bind and/or modulate the activity of the variant protein. These candidate compounds can be further screened against a protein having normal function (e.g., a wild-type/non-variant protein) to further determine the effect of the compound on the protein activity. Furthermore, these compounds can be tested in animal or invertebrate systems to determine in vivo activity/effectiveness. Compounds can be identified that activate (agonists) or inactivate (antagonists) the variant protein, and different compounds can be identified that cause various degrees of activation or inactivation of the variant protein.

Further, the variant proteins can be used to screen a compound for the ability to stimulate or inhibit interaction between the variant protein and a target molecule that normally interacts with the protein. The target can be a ligand, a substrate or a binding partner that the protein normally interacts with (for example, epinephrine or norepinephrine). Such assays typically include the steps of combining the variant protein with a candidate compound under conditions that allow the variant protein, or fragment thereof, to interact with the target molecule, and to detect the formation of a complex between the protein and the target or to detect the biochemical consequence of the interaction with the variant protein and the target, such as any of the associated effects of signal transduction.

Candidate compounds include, for example, 1) peptides such as soluble peptides, including Ig-tailed fusion peptides and members of random peptide libraries (see, e.g., Lam et al., Nature 354:82-84 (1991); Houghten et al., Nature 354:84-86 (1991)) and combinatorial chemistry-derived molecular libraries made of D- and/or L-configuration amino acids; 2) phosphopeptides (e.g., members of random and partially degenerate, directed phosphopeptide libraries, see, e.g., Songyang et al., Cell 72:767-778 (1993)); 3) antibodies (e.g., polyclonal, monoclonal, humanized, anti-idiotypic, chimeric, and single chain antibodies as well as Fab, F(ab′)₂, Fab expression library fragments, and epitope-binding fragments of antibodies); and 4) small organic and inorganic molecules (e.g., molecules obtained from combinatorial and natural product libraries).

One candidate compound is a soluble fragment of the variant protein that competes for ligand binding. Other candidate compounds include mutant proteins or appropriate fragments containing mutations that affect variant protein function and thus compete for ligand. Accordingly, a fragment that competes for ligand, for example with a higher affinity, or a fragment that binds ligand but does not allow release, is encompassed by the invention.

The invention further includes other end point assays to identify compounds that modulate (stimulate or inhibit) variant protein activity. The assays typically involve an assay of events in the signal transduction pathway that indicate protein activity. Thus, the expression of genes that are up or down-regulated in response to the variant protein dependent signal cascade can be assayed. In one embodiment, the regulatory region of such genes can be operably linked to a marker that is easily detectable, such as luciferase. Alternatively, phosphorylation of the variant protein, or a variant protein target, could also be measured. Any of the biological or biochemical functions mediated by the variant protein can be used as an endpoint assay. These include all of the biochemical or biological events described herein, in the references cited herein, incorporated by reference for these endpoint assay targets, and other functions known to those of ordinary skill in the art.

Binding and/or activating compounds can also be screened by using chimeric variant proteins in which an amino terminal extracellular domain or parts thereof, an entire transmembrane domain or subregions, and/or the carboxyl terminal intracellular domain or parts thereof, can be replaced by heterologous domains or subregions. For example, a substrate-binding region can be used that interacts with a different substrate than that which is normally recognized by a variant protein. Accordingly, a different set of signal transduction components is available as an end-point assay for activation. This allows for assays to be performed in other than the specific host cell from which the variant protein is derived.

The variant proteins are also useful in competition binding assays in methods designed to discover compounds that interact with the variant protein. Thus, a compound can be exposed to a variant protein under conditions that allow the compound to bind or to otherwise interact with the variant protein. A binding partner, such as ligand, that normally interacts with the variant protein is also added to the mixture. If the test compound interacts with the variant protein or its binding partner, it decreases the amount of complex formed or activity from the variant protein. This type of assay is particularly useful in screening for compounds that interact with specific regions of the variant protein (Hodgson, Bio/technology, 1992, Sep. 10(9), 973-80).

To perform cell-free drug screening assays, it is sometimes desirable to immobilize either the variant protein or a fragment thereof, or its target molecule, to facilitate separation of complexes from uncomplexed forms of one or both of the proteins, as well as to accommodate automation of the assay. Any method for immobilizing proteins on matrices can be used in drug screening assays. In one embodiment, a fusion protein containing an added domain allows the protein to be bound to a matrix. For example, glutathione-S-transferase/¹²⁵ I fusion proteins can be adsorbed onto glutathione sepharose beads (Sigma Chemical, St. Louis, Mo.) or glutathione derivatized microtitre plates, which are then combined with the cell lysates (e.g., ³⁵S-labeled) and a candidate compound, such as a drug candidate, and the mixture incubated under conditions conducive to complex formation (e.g., at physiological conditions for salt and pH). Following incubation, the beads can be washed to remove any unbound label, and the matrix immobilized and radiolabel determined directly, or in the supernatant after the complexes are dissociated. Alternatively, the complexes can be dissociated from the matrix, separated by SDS-PAGE, and the level of bound material found in the bead fraction quantitated from the gel using standard electrophoretic techniques.

Either the variant protein or its target molecule can be immobilized utilizing conjugation of biotin and streptavidin. Alternatively, antibodies reactive with the variant protein but which do not interfere with binding of the variant protein to its target molecule can be derivatized to the wells of the plate, and the variant protein trapped in the wells by antibody conjugation. Preparations of the target molecule and a candidate compound are incubated in the variant protein-presenting wells and the amount of complex trapped in the well can be quantitated. Methods for detecting such complexes, in addition to those described above for the GST-immobilized complexes, include immunodetection of complexes using antibodies reactive with the protein target molecule, or which are reactive with variant protein and compete with the target molecule, and enzyme-linked assays that rely on detecting an enzymatic activity associated with the target molecule.

Modulators of variant protein activity identified according to these drug screening assays can be used to treat a subject with a disorder mediated by the protein pathway, such as psoriasis. These methods of treatment typically include the steps of administering the modulators of protein activity in a pharmaceutical composition to a subject in need of such treatment.

The variant proteins, or fragments thereof, disclosed herein can themselves be directly used to treat a disorder characterized by an absence of, inappropriate, or unwanted expression or activity of the variant protein. Accordingly, methods for treatment include the use of a variant protein disclosed herein or fragments thereof.

In yet another aspect of the invention, variant proteins can be used as “bait proteins” in a two-hybrid assay or three-hybrid assay (see, e.g., U.S. Pat. No. 5,283,317; Zervos et al. (1993) Cell 72:223-232; Madura et al. (1993) J. Biol. Chem. 268:12046-12054; Bartel et al. (1993) Biotechniques 14:920-924; Iwabuchi et al. (1993) Oncogene 8:1693-1696; and Brent WO94/10300) to identify other proteins that bind to or interact with the variant protein and are involved in variant protein activity. Such variant protein-binding proteins are also likely to be involved in the propagation of signals by the variant proteins or variant protein targets as, for example, elements of a protein-mediated signaling pathway. Alternatively, such variant protein-binding proteins are inhibitors of the variant protein.

The two-hybrid system is based on the modular nature of most transcription factors, which typically consist of separable DNA-binding and activation domains. Briefly, the assay typically utilizes two different DNA constructs. In one construct, the gene that codes for a variant protein is fused to a gene encoding the DNA binding domain of a known transcription factor (e.g., GAL-4). In the other construct, a DNA sequence, from a library of DNA sequences, that encodes an unidentified protein (“prey” or “sample”) is fused to a gene that codes for the activation domain of the known transcription factor. If the “bait” and the “prey” proteins are able to interact, in vivo, forming a variant protein-dependent complex, the DNA-binding and activation domains of the transcription factor are brought into close proximity. This proximity allows transcription of a reporter gene (e.g., LacZ) that is operably linked to a transcriptional regulatory site responsive to the transcription factor. Expression of the reporter gene can be detected, and cell colonies containing the functional transcription factor can be isolated and used to obtain the cloned gene that encodes the protein that interacts with the variant protein.

Antibodies Directed to Variant Proteins

The present invention also provides antibodies that selectively bind to the variant proteins disclosed herein and fragments thereof. Such antibodies may be used to quantitatively or qualitatively detect the variant proteins of the present invention. As used herein, an antibody selectively binds a target variant protein when it binds the variant protein and does not significantly bind to non-variant proteins, i.e., the antibody does not significantly bind to normal, wild-type, or art-known proteins that do not contain a variant amino acid sequence due to one or more SNPs of the present invention (variant amino acid sequences may be due to, for example, nonsynonymous cSNPs, nonsense SNPs that create a stop codon, thereby causing a truncation of a polypeptide or SNPs that cause read-through mutations resulting in an extension of a polypeptide).

As used herein, an antibody is defined in terms consistent with that recognized in the art: they are multi-subunit proteins produced by an organism in response to an antigen challenge. The antibodies of the present invention include both monoclonal antibodies and polyclonal antibodies, as well as antigen-reactive proteolytic fragments of such antibodies, such as Fab, F(ab)′₂, and Fv fragments. In addition, an antibody of the present invention further includes any of a variety of engineered antigen-binding molecules such as a chimeric antibody (U.S. Pat. Nos. 4,816,567 and 4,816,397; Morrison et al., Proc. Natl. Acad. Sci. USA, 81:6851, 1984; Neuberger et al., Nature 312:604, 1984), a humanized antibody (U.S. Pat. Nos. 5,693,762; 5,585,089; and 5,565,332), a single-chain Fv (U.S. Pat. No. 4,946,778; Ward et al., Nature 334:544, 1989), a bispecific antibody with two binding specificities (Segal et al., J. Immunol. Methods 248:1, 2001; Carter, J. Immunol. Methods 248:7, 2001), a diabody, a triabody, and a tetrabody (Todorovska et al., J. Immunol. Methods, 248:47, 2001), as well as a Fab conjugate (dimer or trimer), and a minibody.

Many methods are known in the art for generating and/or identifying antibodies to a given target antigen (Harlow, Antibodies, Cold Spring Harbor Press, (1989)). In general, an isolated peptide (e.g., a variant protein of the present invention) is used as an immunogen and is administered to a mammalian organism, such as a rat, rabbit, hamster or mouse. Either a full-length protein, an antigenic peptide fragment (e.g., a peptide fragment containing a region that varies between a variant protein and a corresponding wild-type protein), or a fusion protein can be used. A protein used as an immunogen may be naturally-occurring, synthetic or recombinantly produced, and may be administered in combination with an adjuvant, including but not limited to, Freund's (complete and incomplete), mineral gels such as aluminum hydroxide, surface active substance such as lysolecithin, pluronic polyols, polyanions, peptides, oil emulsions, keyhole limpet hemocyanin, dinitrophenol, and the like.

Monoclonal antibodies can be produced by hybridoma technology (Kohler and Milstein, Nature, 256:495, 1975), which immortalizes cells secreting a specific monoclonal antibody. The immortalized cell lines can be created in vitro by fusing two different cell types, typically lymphocytes, and tumor cells. The hybridoma cells may be cultivated in vitro or in vivo. Additionally, fully human antibodies can be generated by transgenic animals (He et al., J. Immunol., 169:595, 2002). Fd phage and Fd phagemid technologies may be used to generate and select recombinant antibodies in vitro (Hoogenboom and Chames, Immunol. Today 21:371, 2000; Liu et al., J. Mol. Biol. 315:1063, 2002). The complementarity-determining regions of an antibody can be identified, and synthetic peptides corresponding to such regions may be used to mediate antigen binding (U.S. Pat. No. 5,637,677).

Antibodies are preferably prepared against regions or discrete fragments of a variant protein containing a variant amino acid sequence as compared to the corresponding wild-type protein (e.g., a region of a variant protein that includes an amino acid encoded by a nonsynonymous cSNP, a region affected by truncation caused by a nonsense SNP that creates a stop codon, or a region resulting from the destruction of a stop codon due to read-through mutation caused by a SNP). Furthermore, preferred regions will include those involved in function/activity and/or protein/binding partner interaction. Such fragments can be selected on a physical property, such as fragments corresponding to regions that are located on the surface of the protein, e.g., hydrophilic regions, or can be selected based on sequence uniqueness, or based on the position of the variant amino acid residue(s) encoded by the SNPs provided by the present invention. An antigenic fragment will typically comprise at least about 8-10 contiguous amino acid residues in which at least one of the amino acid residues is an amino acid affected by a SNP disclosed herein. The antigenic peptide can comprise, however, at least 12, 14, 16, 20, 25, 50, 100 (or any other number in-between) or more amino acid residues, provided that at least one amino acid is affected by a SNP disclosed herein.

Detection of an antibody of the present invention can be facilitated by coupling (i.e., physically linking) the antibody or an antigen-reactive fragment thereof to a detectable substance. Detectable substances include, but are not limited to, various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, β-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein; fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include ¹²⁵I, ¹³¹I, ³⁵S or ³H.

Antibodies, particularly the use of antibodies as therapeutic agents, are reviewed in: Morgan, “Antibody therapy for Alzheimer's disease”, Expert Rev Vaccines. 2003 February; 2(1):53-9; Ross et al., “Anticancer antibodies”, Am J Clin Pathol. 2003 April; 119(4):472-85; Goldenberg, “Advancing role of radiolabeled antibodies in the therapy of cancer”, Cancer Immunol Immunother. 2003 May; 52(5):281-96. Epub 2003 Mar. 11; Ross et al., “Antibody-based therapeutics in oncology”, Expert Rev Anticancer Ther. 2003 February; 3(1): 107-21; Cao et al., “Bispecific antibody conjugates in therapeutics”, Adv Drug Deliv Rev. 2003 Feb. 10; 55(2): 171-97; von Mehren et al., “Monoclonal antibody therapy for cancer”, Annu Rev Med. 2003; 54:343-69. Epub 2001 Dec. 03; Hudson et al., “Engineered antibodies”, Nat. Med. 2003 January; 9(1):129-34; Brekke et al., “Therapeutic antibodies for human diseases at the dawn of the twenty-first century”, Nat Rev Drug Discov. 2003 January; 2(1):52-62 (Erratum in: Nat Rev Drug Discov. 2003 March; 2(3):240); Houdebine, “Antibody manufacture in transgenic animals and comparisons with other systems”, Curr Opin Biotechnol. 2002 December; 13(6):625-9; Andreakos et al., “Monoclonal antibodies in immune and inflammatory diseases”, Curr Opin Biotechnol. 2002 December; 13(6):615-20; Kellermann et al., “Antibody discovery: the use of transgenic mice to generate human monoclonal antibodies for therapeutics”, Curr Opin Biotechnol. 2002 December; 13(6):593-7; Pini et al., “Phage display and colony filter screening for high-throughput selection of antibody libraries”, Comb Chem High Throughput Screen. 2002 November; 5(7):503-10; Batra et al., “Pharmacokinetics and biodistribution of genetically engineered antibodies”, Curr Opin Biotechnol. 2002 December; 13(6):603-8; and Tangri et al., “Rationally engineered proteins or antibodies with absent or reduced immunogenicity”, Curr Med. Chem. 2002 December; 9(24):2191-9.

Uses of Antibodies

Antibodies can be used to isolate the variant proteins of the present invention from a natural cell source or from recombinant host cells by standard techniques, such as affinity chromatography or immunoprecipitation. In addition, antibodies are useful for detecting the presence of a variant protein of the present invention in cells or tissues to determine the pattern of expression of the variant protein among various tissues in an organism and over the course of normal development or disease progression. Further, antibodies can be used to detect variant protein in situ, in vitro, in a bodily fluid, or in a cell lysate or supernatant in order to evaluate the amount and pattern of expression. Also, antibodies can be used to assess abnormal tissue distribution, abnormal expression during development, or expression in an abnormal condition, such as psoriasis. Additionally, antibody detection of circulating fragments of the full-length variant protein can be used to identify turnover.

Antibodies to the variant proteins of the present invention are also useful in pharmacogenomic analysis. Thus, antibodies against variant proteins encoded by alternative SNP alleles can be used to identify individuals that require modified treatment modalities.

Further, antibodies can be used to assess expression of the variant protein in disease states such as in active stages of the disease or in an individual with a predisposition to a disease related to the protein's function, particularly psoriasis. Antibodies specific for a variant protein encoded by a SNP-containing nucleic acid molecule of the present invention can be used to assay for the presence of the variant protein, such as to screen for predisposition to psoriasis as indicated by the presence of the variant protein.

Antibodies are also useful as diagnostic tools for evaluating the variant proteins in conjunction with analysis by electrophoretic mobility, isoelectric point, tryptic peptide digest, and other physical assays well known in the art.

Antibodies are also useful for tissue typing. Thus, where a specific variant protein has been correlated with expression in a specific tissue, antibodies that are specific for this protein can be used to identify a tissue type.

Antibodies can also be used to assess aberrant subcellular localization of a variant protein in cells in various tissues. The diagnostic uses can be applied, not only in genetic testing, but also in monitoring a treatment modality. Accordingly, where treatment is ultimately aimed at correcting the expression level or the presence of variant protein or aberrant tissue distribution or developmental expression of a variant protein, antibodies directed against the variant protein or relevant fragments can be used to monitor therapeutic efficacy.

The antibodies are also useful for inhibiting variant protein function, for example, by blocking the binding of a variant protein to a binding partner. These uses can also be applied in a therapeutic context in which treatment involves inhibiting a variant protein's function. An antibody can be used, for example, to block or competitively inhibit binding, thus modulating (agonizing or antagonizing) the activity of a variant protein. Antibodies can be prepared against specific variant protein fragments containing sites required for function or against an intact variant protein that is associated with a cell or cell membrane. For in vivo administration, an antibody may be linked with an additional therapeutic payload such as a radionuclide, an enzyme, an immunogenic epitope, or a cytotoxic agent. Suitable cytotoxic agents include, but are not limited to, bacterial toxin such as diphtheria, and plant toxin such as ricin. The in vivo half-life of an antibody or a fragment thereof may be lengthened by pegylation through conjugation to polyethylene glycol (Leong et al., Cytokine 16:106, 2001).

The invention also encompasses kits for using antibodies, such as kits for detecting the presence of a variant protein in a test sample. An exemplary kit can comprise antibodies such as a labeled or labelable antibody and a compound or agent for detecting variant proteins in a biological sample; means for determining the amount, or presence/absence of variant protein in the sample; means for comparing the amount of variant protein in the sample with a standard; and instructions for use.

Vectors and Host Cells

The present invention also provides vectors containing the SNP-containing nucleic acid molecules described herein. The term “vector” refers to a vehicle, preferably a nucleic acid molecule, which can transport a SNP-containing nucleic acid molecule. When the vector is a nucleic acid molecule, the SNP-containing nucleic acid molecule can be covalently linked to the vector nucleic acid. Such vectors include, but are not limited to, a plasmid, single or double stranded phage, a single or double stranded RNA or DNA viral vector, or artificial chromosome, such as a BAC, PAC, YAC, or MAC.

A vector can be maintained in a host cell as an extrachromosomal element where it replicates and produces additional copies of the SNP-containing nucleic acid molecules. Alternatively, the vector may integrate into the host cell genome and produce additional copies of the SNP-containing nucleic acid molecules when the host cell replicates.

The invention provides vectors for the maintenance (cloning vectors) or vectors for expression (expression vectors) of the SNP-containing nucleic acid molecules. The vectors can function in prokaryotic or eukaryotic cells or in both (shuttle vectors).

Expression vectors typically contain cis-acting regulatory regions that are operably linked in the vector to the SNP-containing nucleic acid molecules such that transcription of the SNP-containing nucleic acid molecules is allowed in a host cell. The SNP-containing nucleic acid molecules can also be introduced into the host cell with a separate nucleic acid molecule capable of affecting transcription. Thus, the second nucleic acid molecule may provide a trans-acting factor interacting with the cis-regulatory control region to allow transcription of the SNP-containing nucleic acid molecules from the vector. Alternatively, a trans-acting factor may be supplied by the host cell. Finally, a trans-acting factor can be produced from the vector itself. It is understood, however, that in some embodiments, transcription and/or translation of the nucleic acid molecules can occur in a cell-free system.

The regulatory sequences to which the SNP-containing nucleic acid molecules described herein can be operably linked include promoters for directing mRNA transcription. These include, but are not limited to, the left promoter from bacteriophage λ, the lac, TRP, and TAC promoters from E. coli, the early and late promoters from SV40, the CMV immediate early promoter, the adenovirus early and late promoters, and retrovirus long-terminal repeats.

In addition to control regions that promote transcription, expression vectors may also include regions that modulate transcription, such as repressor binding sites and enhancers. Examples include the SV40 enhancer, the cytomegalovirus immediate early enhancer, polyoma enhancer, adenovirus enhancers, and retrovirus LTR enhancers.

In addition to containing sites for transcription initiation and control, expression vectors can also contain sequences necessary for transcription termination and, in the transcribed region, a ribosome-binding site for translation. Other regulatory control elements for expression include initiation and termination codons as well as polyadenylation signals. A person of ordinary skill in the art would be aware of the numerous regulatory sequences that are useful in expression vectors (see, e.g., Sambrook and Russell, 2000, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.).

A variety of expression vectors can be used to express a SNP-containing nucleic acid molecule. Such vectors include chromosomal, episomal, and virus-derived vectors, for example, vectors derived from bacterial plasmids, from bacteriophage, from yeast episomes, from yeast chromosomal elements, including yeast artificial chromosomes, from viruses such as baculoviruses, papovaviruses such as SV40, Vaccinia viruses, adenoviruses, poxviruses, pseudorabies viruses, and retroviruses. Vectors can also be derived from combinations of these sources such as those derived from plasmid and bacteriophage genetic elements, e.g., cosmids and phagemids. Appropriate cloning and expression vectors for prokaryotic and eukaryotic hosts are described in Sambrook and Russell, 2000, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.

The regulatory sequence in a vector may provide constitutive expression in one or more host cells (e.g., tissue specific expression) or may provide for inducible expression in one or more cell types such as by temperature, nutrient additive, or exogenous factor, e.g., a hormone or other ligand. A variety of vectors that provide constitutive or inducible expression of a nucleic acid sequence in prokaryotic and eukaryotic host cells are well known to those of ordinary skill in the art.

A SNP-containing nucleic acid molecule can be inserted into the vector by methodology well-known in the art. Generally, the SNP-containing nucleic acid molecule that will ultimately be expressed is joined to an expression vector by cleaving the SNP-containing nucleic acid molecule and the expression vector with one or more restriction enzymes and then ligating the fragments together. Procedures for restriction enzyme digestion and ligation are well known to those of ordinary skill in the art.

The vector containing the appropriate nucleic acid molecule can be introduced into an appropriate host cell for propagation or expression using well-known techniques. Bacterial host cells include, but are not limited to, E. coli, Streptomyces, and Salmonella typhimurium. Eukaryotic host cells include, but are not limited to, yeast, insect cells such as Drosophila, animal cells such as COS and CHO cells, and plant cells.

As described herein, it may be desirable to express the variant peptide as a fusion protein. Accordingly, the invention provides fusion vectors that allow for the production of the variant peptides. Fusion vectors can, for example, increase the expression of a recombinant protein, increase the solubility of the recombinant protein, and aid in the purification of the protein by acting, for example, as a ligand for affinity purification. A proteolytic cleavage site may be introduced at the junction of the fusion moiety so that the desired variant peptide can ultimately be separated from the fusion moiety. Proteolytic enzymes suitable for such use include, but are not limited to, factor Xa, thrombin, and enterokinase. Typical fusion expression vectors include pGEX (Smith et al., Gene 67:31-40 (1988)), pMAL (New England Biolabs, Beverly, Mass.) and pRIT5 (Pharmacia, Piscataway, N.J.) which fuse glutathione S-transferase (GST), maltose E binding protein, or protein A, respectively, to the target recombinant protein. Examples of suitable inducible non-fusion E. coli expression vectors include pTrc (Amann et al., Gene 69:301-415 (1988)) and pET 11d (Studier et al., Gene Expression Technology: Methods in Enzymology 185:60-89 (1990)).

Recombinant protein expression can be maximized in a bacterial host by providing a genetic background wherein the host cell has an impaired capacity to proteolytically cleave the recombinant protein (Gottesman, S., Gene Expression Technology: Methods in Enzymology 185, Academic Press, San Diego, Calif. (1990) 119-128). Alternatively, the sequence of the SNP-containing nucleic acid molecule of interest can be altered to provide preferential codon usage for a specific host cell, for example, E. coli (Wada et al., Nucleic Acids Res. 20:2111-2118 (1992)).

The SNP-containing nucleic acid molecules can also be expressed by expression vectors that are operative in yeast. Examples of vectors for expression in yeast (e.g., S. cerevisiae) include pYepSec1 (Baldari, et al., EMBO J. 6:229-234 (1987)), pMFa (Kurjan et al., Cell 30:933-943 (1982)), pJRY88 (Schultz et al., Gene 54:113-123 (1987)), and pYES2 (Invitrogen Corporation, San Diego, Calif.).

The SNP-containing nucleic acid molecules can also be expressed in insect cells using, for example, baculovirus expression vectors. Baculovirus vectors available for expression of proteins in cultured insect cells (e.g., Sf 9 cells) include the pAc series (Smith et al., Mol. Cell. Biol. 3:2156-2165 (1983)) and the pVL series (Lucklow et al., Virology 170:31-49 (1989)).

In certain embodiments of the invention, the SNP-containing nucleic acid molecules described herein are expressed in mammalian cells using mammalian expression vectors. Examples of mammalian expression vectors include pCDM8 (Seed, B. Nature 329:840 (1987)) and pMT2PC (Kaufman et al., EMBO J. 6:187-195 (1987)).

The invention also encompasses vectors in which the SNP-containing nucleic acid molecules described herein are cloned into the vector in reverse orientation, but operably linked to a regulatory sequence that permits transcription of antisense RNA. Thus, an antisense transcript can be produced to the SNP-containing nucleic acid sequences described herein, including both coding and non-coding regions. Expression of this antisense RNA is subject to each of the parameters described above in relation to expression of the sense RNA (regulatory sequences, constitutive or inducible expression, tissue-specific expression).

The invention also relates to recombinant host cells containing the vectors described herein. Host cells therefore include, for example, prokaryotic cells, lower eukaryotic cells such as yeast, other eukaryotic cells such as insect cells, and higher eukaryotic cells such as mammalian cells.

The recombinant host cells can be prepared by introducing the vector constructs described herein into the cells by techniques readily available to persons of ordinary skill in the art. These include, but are not limited to, calcium phosphate transfection, DEAE-dextran-mediated transfection, cationic lipid-mediated transfection, electroporation, transduction, infection, lipofection, and other techniques such as those described in Sambrook and Russell, 2000, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.).

Host cells can contain more than one vector. Thus, different SNP-containing nucleotide sequences can be introduced in different vectors into the same cell. Similarly, the SNP-containing nucleic acid molecules can be introduced either alone or with other nucleic acid molecules that are not related to the SNP-containing nucleic acid molecules, such as those providing trans-acting factors for expression vectors. When more than one vector is introduced into a cell, the vectors can be introduced independently, co-introduced, or joined to the nucleic acid molecule vector.

In the case of bacteriophage and viral vectors, these can be introduced into cells as packaged or encapsulated virus by standard procedures for infection and transduction. Viral vectors can be replication-competent or replication-defective. In the case in which viral replication is defective, replication can occur in host cells that provide functions that complement the defects.

Vectors generally include selectable markers that enable the selection of the subpopulation of cells that contain the recombinant vector constructs. The marker can be inserted in the same vector that contains the SNP-containing nucleic acid molecules described herein or may be in a separate vector. Markers include, for example, tetracycline or ampicillin-resistance genes for prokaryotic host cells, and dihydrofolate reductase or neomycin resistance genes for eukaryotic host cells. However, any marker that provides selection for a phenotypic trait can be effective.

While the mature variant proteins can be produced in bacteria, yeast, mammalian cells, and other cells under the control of the appropriate regulatory sequences, cell-free transcription and translation systems can also be used to produce these variant proteins using RNA derived from the DNA constructs described herein.

Where secretion of the variant protein is desired, which is difficult to achieve with multi-transmembrane domain containing proteins such as G-protein-coupled receptors (GPCRs), appropriate secretion signals can be incorporated into the vector. The signal sequence can be endogenous to the peptides or heterologous to these peptides.

Where the variant protein is not secreted into the medium, the protein can be isolated from the host cell by standard disruption procedures, including freeze/thaw, sonication, mechanical disruption, use of lysing agents, and the like. The variant protein can then be recovered and purified by well-known purification methods including, for example, ammonium sulfate precipitation, acid extraction, anion or cationic exchange chromatography, phosphocellulose chromatography, hydrophobic-interaction chromatography, affinity chromatography, hydroxylapatite chromatography, lectin chromatography, or high performance liquid chromatography.

It is also understood that, depending upon the host cell in which recombinant production of the variant proteins described herein occurs, they can have various glycosylation patterns, or may be non-glycosylated, as when produced in bacteria. In addition, the variant proteins may include an initial modified methionine in some cases as a result of a host-mediated process.

For further information regarding vectors and host cells, see Current Protocols in Molecular Biology, John Wiley & Sons, N.Y.

Uses of Vectors and Host Cells, and Transgenic Animals

Recombinant host cells that express the variant proteins described herein have a variety of uses. For example, the cells are useful for producing a variant protein that can be further purified into a preparation of desired amounts of the variant protein or fragments thereof. Thus, host cells containing expression vectors are useful for variant protein production.

Host cells are also useful for conducting cell-based assays involving the variant protein or variant protein fragments, such as those described above as well as other formats known in the art. Thus, a recombinant host cell expressing a variant protein is useful for assaying compounds that stimulate or inhibit variant protein function. Such an ability of a compound to modulate variant protein function may not be apparent from assays of the compound on the native/wild-type protein, or from cell-free assays of the compound. Recombinant host cells are also useful for assaying functional alterations in the variant proteins as compared with a known function.

Genetically-engineered host cells can be further used to produce non-human transgenic animals. A transgenic animal is preferably a non-human mammal, for example, a rodent, such as a rat or mouse, in which one or more of the cells of the animal include a transgene. A transgene is exogenous DNA containing a SNP of the present invention which is integrated into the genome of a cell from which a transgenic animal develops and which remains in the genome of the mature animal in one or more of its cell types or tissues. Such animals are useful for studying the function of a variant protein in vivo, and identifying and evaluating modulators of variant protein activity. Other examples of transgenic animals include, but are not limited to, non-human primates, sheep, dogs, cows, goats, chickens, and amphibians. Transgenic non-human mammals such as cows and goats can be used to produce variant proteins which can be secreted in the animal's milk and then recovered.

A transgenic animal can be produced by introducing a SNP-containing nucleic acid molecule into the male pronuclei of a fertilized oocyte, e.g., by microinjection or retroviral infection, and allowing the oocyte to develop in a pseudopregnant female foster animal. Any nucleic acid molecules that contain one or more SNPs of the present invention can potentially be introduced as a transgene into the genome of a non-human animal.

Any of the regulatory or other sequences useful in expression vectors can form part of the transgenic sequence. This includes intronic sequences and polyadenylation signals, if not already included. A tissue-specific regulatory sequence(s) can be operably linked to the transgene to direct expression of the variant protein in particular cells or tissues.

Methods for generating transgenic animals via embryo manipulation and microinjection, particularly animals such as mice, have become conventional in the art and are described in, for example, U.S. Pat. Nos. 4,736,866 and 4,870,009, both by Leder et al., U.S. Pat. No. 4,873,191 by Wagner et al., and in Hogan, B., Manipulating the Mouse Embryo, (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 1986). Similar methods are used for production of other transgenic animals. A transgenic founder animal can be identified based upon the presence of the transgene in its genome and/or expression of transgenic mRNA in tissues or cells of the animals. A transgenic founder animal can then be used to breed additional animals carrying the transgene. Moreover, transgenic animals carrying a transgene can further be bred to other transgenic animals carrying other transgenes. A transgenic animal also includes a non-human animal in which the entire animal or tissues in the animal have been produced using the homologously recombinant host cells described herein.

In another embodiment, transgenic non-human animals can be produced which contain selected systems that allow for regulated expression of the transgene. One example of such a system is the cre/loxP recombinase system of bacteriophage P1 (Lakso et al. PNAS 89:6232-6236 (1992)). Another example of a recombinase system is the FLP recombinase system of S. cerevisiae (O'Gorman et al. Science 251:1351-1355 (1991)). If a cre/loxP recombinase system is used to regulate expression of the transgene, animals containing transgenes encoding both the Cre recombinase and a selected protein are generally needed. Such animals can be provided through the construction of “double” transgenic animals, e.g., by mating two transgenic animals, one containing a transgene encoding a selected variant protein and the other containing a transgene encoding a recombinase.

Clones of the non-human transgenic animals described herein can also be produced according to the methods described in, for example, Wilmut, I. et al. Nature 385:810-813 (1997) and PCT International Publication Nos. WO 97/07668 and WO 97/07669. In brief, a cell (e.g., a somatic cell) from the transgenic animal can be isolated and induced to exit the growth cycle and enter G_(o) phase. The quiescent cell can then be fused, e.g., through the use of electrical pulses, to an enucleated oocyte from an animal of the same species from which the quiescent cell is isolated. The reconstructed oocyte is then cultured such that it develops to morula or blastocyst and then transferred to pseudopregnant female foster animal. The offspring born of this female foster animal will be a clone of the animal from which the cell (e.g., a somatic cell) is isolated.

Transgenic animals containing recombinant cells that express the variant proteins described herein are useful for conducting the assays described herein in an in vivo context. Accordingly, the various physiological factors that are present in vivo and that could influence ligand or substrate binding, variant protein activation, signal transduction, or other processes or interactions, may not be evident from in vitro cell-free or cell-based assays. Thus, non-human transgenic animals of the present invention may be used to assay in vivo variant protein function as well as the activities of a therapeutic agent or compound that modulates variant protein function/activity or expression. Such animals are also suitable for assessing the effects of null mutations (i.e., mutations that substantially or completely eliminate one or more variant protein functions).

For further information regarding transgenic animals, see Houdebine, “Antibody manufacture in transgenic animals and comparisons with other systems”, Curr Opin Biotechnol. 2002 December; 1 3(6):625-9; Petters et al., “Transgenic animals as models for human disease”, Transgenic Res. 2000; 9(4-5):347-51; discussion 345-6; Wolf et al., “Use of transgenic animals in understanding molecular mechanisms of toxicity”, J Pharm Pharmacol. 1998 June; 50(6):567-74; Echelard, “Recombinant protein production in transgenic animals”, Curr Opin Biotechnol. 1996 October; 7(5):536-40; Houdebine, “Transgenic animal bioreactors”, Transgenic Res. 2000; 9(4-5):305-20; Pirity et al., “Embryonic stem cells, creating transgenic animals”, Methods Cell Biol. 1998; 57:279-93; and Robl et al., “Artificial chromosome vectors and expression of complex proteins in transgenic animals”, Theriogenology. 2003 Jan. 1; 59 (1):107-13.

EXAMPLES

The following examples are offered to illustrate, but not to limit, the claimed invention.

Example One Statistical Analysis of SNPs Associated with Psoriasis

Overview

A multi-tiered genetic association study of 25,215 SNPs in three case-control sample sets (1,446 patients and 1,432 controls) identified three IL13-linked SNPs (rs1800925, rs20541, rs848) associated with psoriasis. These SNPs had psoriasis susceptibility effects (joint allelic ORs: 0.76→0.78; P_(comb): 1.3E-03→2.50E-04), and the association patterns were consistent across the sample sets, with the minor alleles being protective. Haplotype analyses identified one common, susceptible haplotype CCG (joint allelic OR=1.27; P_(comb)=1.88E-04) and a less common, protective haplotype TTT (joint allelic OR=0.74; P_(comb)=7.05E-04). In combination with the other known genetic risk factors, HLA-C, IL12B and IL23R, the variants reported here generate an 11-fold psoriasis-risk differential. Residing in the 5q31 cytokine gene cluster, IL13 encodes an important T-cell derived cytokine that regulates cell-mediated immunity.

Results and Discussion

Three SNPs within 4 kb of one another in the IL13-region in 5q31 met our significance criteria in the pooled-genotyping phase of our study: rs1800925 (located 1 kb 5′ of the IL13 coding region), rs20541 (an IL13 exon 4 missense SNP, Q144R), and rs848 (located within the 3′ UTR region of IL13).

Individual genotyping of these three markers in all three sample sets showed each of the markers was consistent with Hardy-Weinberg equilibrium in both cases and controls of each sample set (Table 5). Minor allele frequencies for all three SNPs were nearly the same: approximately 18-20% in controls and decreasing to 14-17% in cases. For all three SNPs, combining P-values across the three sample sets using Fisher's combined probability method generated significant values for both allelic and genotypic tests (allelic: P_(rs1800925)=2.50E-04, P_(rs20541)=0.0013, P_(rs848)=0.0017; genotypic P_(rs1800925)=0.0029, P_(rs2041)=0.0022, P_(rs848)=0.0027). Notably, the 5′ SNP, rs1800925, and the 3′ UTR SNP, rs848, were significant (P_(allelic)<0.05) in all three sample sets.

Pairwise LD values between these three associated SNPs were calculated, using the r² measure applied to all individuals in sample set 1. Two of the SNPs, rs20541 and rs848, were in very high LD (r²=0.98). The remaining SNP, rs1800925 was not as highly correlated with the other two SNPs (r²=0.24 for both comparisons).

Haplotype analyses can often reveal important cis-acting effects among alleles at closely-linked sites. We used the program Haplo.Stats (Schaid D J, Rowland C M, Tines D E, Jacobson R M, Poland G A. Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet. 2002; 70: 425-434) to estimate haplotypes for these three SNPs and to perform a test of psoriasis association. This program calculates P-values for each haplotype and also a global P-value. Results for the three SNPs (rs1800925, rs20541 and rs848) showed four common haplotypes in each sample set (as expected from the LD patterns), but only the CCG haplotype (carrying the major allele at each site) was significant in all three studies (SS1 P=0.0288, SS2 P=0.0177, and SS3 P=0.0037) (Table 6). A combined analysis showed association for this haplotype (P_(comb)=1.84E-04) and also revealed a second associated haplotype, TTT (P_(comb)=7.05E-04) which carries the minor allele at each site and exhibited protective effects. Overall, the susceptible haplotype, CCG, had a frequency of 72.1% in controls increasing to 76.5% in cases while the protective haplotype, TTT, had a frequency of 11.5% in controls decreasing to 8.8% in cases. The risk CCG haplotype had a joint OR=1.27 and the protective haplotype had a joint OR=0.74.

IL-13 is an immuno-regulatory cytokine produced primarily by activated Th2 cells that exerts its effect by binding to its receptor and activating the STAT6-mediated signal transduction pathway. IL-13 has been most prominently implicated as a critical mediator of an allergic inflammatory response, and genetic variants of IL13 are known to be associated with susceptibility to asthma in humans (Heinzmann A, Mao X Q, Akaiwa M, Kreomer R T, Gao P S, Ohshima K et al. Genetic variants of IL-13 signalling and human asthma and atopy. Hum Mol Genet. 2000; 9: 549-559). Genetic variation in IL13 has also been associated with the risk of atopic dermatitis in a Japanese study (Tsunemi Y, Saeki H, Nakamura K, Sekiya T, Hirai K, Kakinuma T et al. Interleukin-13 gene polymorphism G4257A is associated with atopic dermatitis in Japanese patients. J Dermatol Sci 2002; 30: 100-107) and although there has been no prior report for a role of IL-13 in psoriasis, altered expression of the two IL-13 receptor chains, IL4-Rα and IL-13α1, has been observed in the skin of psoriasis patients (Cancino-Diaz J C, Reyes-Maldonado E, Banuelos-Panuco C A, Jimenez-Zamudio L, Garcia-Latorre E, Leon-Dorantes G et al. Interleukin-13 receptor in psoriatic keratinocytes: overexpression of the mRNA and underexpression of the protein. J Invest Dermatol 2002; 119: 1114-1120). Thus IL13 represents a reasonable biological candidate gene for the development of psoriasis.

However, as the initial single marker results were derived from a screen of a large number of SNPs, false positive results due to selection bias from multiple testing must be considered. Given the extremely limited number of loci tested in the third sample set (N=three loci−IL12B, IL23R and IL13), our results argue against a false positive error. Using the Dunn-Sidak method of calculating an experimentwise P-value (Sokal R R, Rohlf F J. Biometry 3^(rd) ed. W.H. Freeman and Company, New York, 1995) for the third sample set, analysis of data at the 5′ marker, rs1800925, shows it retains statistical significance after adjusting for multiple testing (P_(adj)=0.0199). In addition, although simulation results (not shown) indicate that these results could have been generated by a truly associated marker, there is still a reasonable chance that the observed results arose from a null model. Finally, previous analyses of population stratification in these sample sets suggested that the confounding impact on the genetic association results was marginal (Cargill M, Schrodi S J, Chang M, Garcia V E, Brandon R, Callis K P et al. A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet. 2007; 80:273-290).

Three other gene regions with substantial and replicated association evidence—HLA-C, IL12B, and IL23R—have been shown to affect the risk of psoriasis. Given the modest effect sizes of these and other gene variants associated with common diseases, multi-locus estimates of differential risk are informative measures (Janssens A C, Moonesinghe R, Yang Q, Steyerberg E W, van Duijn C M, Khoury M J. The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases. Genet Med 2007; 9: 528-535). In an effort to better elucidate the strength of the psoriasis-predisposing effects from all four loci, we estimated the probability of psoriasis given genotypes at HLA-C and the cytokine-pathway genes IL12B, IL23R and IL13. Conditional independence between loci was assumed (three independent methods substantiated this assumption, see the Brief Description of the Figure section) and we employed Bayes' theorem for the calculation. Assuming a psoriasis-prevalence of 3%, these calculations show that the differential risk between the Very High and Low risk multi-locus genotype groups is over 11-fold (FIG. 1; Table 7). Greater than 10% of the general North American white population carry gene combinations which increase their risk of psoriasis to over 0.08; whereas 33% of the North American white population are at less than half the risk of disease compared to the general population.

The 5q31 region harboring IL13 has been identified as a susceptibility locus for psoriasis in a previous family-based linkage study of psoriasis (Friberg C, Bjorck K, Nilsson S, Inerot A, Wahlstrom J, Samuelsson, L. Analysis of chromosome 5q31-42 and psoriasis: confirmation of a susceptibility locus but no association with SNPs within SLC22A4 and SLC22A5. J Invest Dermatol 2006; 126: 998-1002), as well as for other inflammatory conditions including Crohn's disease (Rioux J D, Silverberg M S, Daly M J, Steinhart A H, McLeod R S, Griffiths A M et al. Genomewide search in Canadian families with inflammatory bowel disease reveals two novel susceptibility loci. Am J Hum Genet. 2000; 66: 1863-1870; Rioux J D, Daly M J, Silverberg M S, Lindblad K, Steinhart H, Cohen Z et al. Genetic variation in the 5q31 cytokine gene cluster confers susceptibility to Crohn disease. Nat Genet. 2001; 29: 223-228; Mirza M M, Fisher S A, King K, Cuthbert A P, Hampe J, Sanderson J et al. Genetic evidence for interaction of the 5q31 cytokine locus and the CARD15 gene in Crohn disease. Am J Hum Genet. 2003; 72: 1018-1022; Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007; 447: 661-678) and bronchial hyperresponsiveness/atopy/asthma phenotypes (Heinzmann A, Mao X Q, Akaiwa M, Kreomer R T, Gao P S, Ohshima K et al. Genetic variants of IL-13 signalling and human asthma and atopy. Hum Mol Genet. 2000; 9: 549-559; Postma D S, Bleecker E R, Amelung P J, Holroyd K J, Xu J, Panhuysen C I M et al. Genetic susceptibility to asthma: bronchial hyperresponsiveness coinherited with a major gene for atopy. New Eng J Med 1995; 333: 894-900; Howard T D, Whittaker P A, Zaiman A L, Koppelman G H, Xu J, Hanley M T et al. Identification and association of polymorphisms in the interleukin-13 gene with asthma and atopy in a Dutch population. Am J Respir Cell Mol Biol 2001; 25: 377-384). The link between psoriasis and Crohn's disease appears to be particularly strong, as evidenced by (i) a similar aberrant response in T-helper cells (Neimann A L, Porter S B, Gelfand J M. The epidemiology of psoriasis. Expert Rev Dermatol 2006; 1: 63-75), (ii) familial clustering of the two diseases (Neimann A L, Porter S B, Gelfand J M. The epidemiology of psoriasis. Expert Rev Dermatol 2006; 1: 63-75), and (iii) the observation that the same allele in the IL23R SNP, rs11209026, is associated with risk for both diseases (Cargill M, Schrodi S J, Chang M, Garcia V E, Brandon R, Callis K P et al. A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet. 2007; 80:273-290; Duerr R H, Taylor K D, Brant S R, Rioux J D, Silverberg M S, Daly M J et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science 2006; 314: 1461-1463). Genes contributing to asthma and psoriasis risk, such as ADAM33, also appear to overlap (Lesueur F, Oudot T, Heath S, Foglio M, Lathrop M, Prud'homme J F et al. ADAM33, a new candidate for psoriasis susceptibility. PLoS ONE 2007; 2:e906).

In conclusion, we have presented results of three IL13 SNPs associated with psoriasis. A common susceptibility haplotype, CCG, carried by 92% of controls produced a consistent effect (joint OR=1.27) that achieved statistical significance (P_(comb)=1.88E-04). Conversely, a less-frequent haplotype, TTT, generates protective effects in our study (joint OR=0.74) with fairly similar significance (P_(comb)=7.05E-04). Further, combining the genotypic effects at the SNP directly 5′ of IL13 (rs1800925) with previously-reported susceptibility variants at HLA-C, IL12B and IL23R demonstrates a range of psoriasis relative risk from 0.34, increasing 11-fold to 3.83.

Example Two Statistical Analysis of Additional SNPs Associated with Psoriasis

As disclosed above in Example One, we described that three SNPs (rs1800925, rs20541 and rs848) in the IL13-region at the 3′ end of the 5q31 cytokine gene cluster were associated with psoriasis in a large, multi-tiered, genetic association study (Chang, M., Li, Y., Yan, C., Callis-Duffin, K. P., Matsunami, N., Garcia, V. E., Cargill, M., Civello, D., Bui, N., Catanese, J. J., Leppert, M. F. et al. (2008) Variants in the 5q31 cytokine gene cluster are associated with psoriasis. Genes Immun, doi:10.1038/sj.gene.6364451). The 5q31 genomic region contains a cluster of cytokine and immune-related genes, including interleukin (IL) genes IL3, IL4, IL5, and IL13, interferon regulatory factor-1 (IRF-1), colony-stimulating factor-2 (CSF2) and T-cell transcription factor-7 (TCF7), all excellent biological candidate genes. This region shows extended marker-marker correlation; for example, examination of the CEU HapMap dataset (www.hapmap.org) reveals that a number of markers within the 100 kb interval immediately upstream of IL13 are in relatively strong LD (r²>0.5) with one of the significant IL13 SNPs, rs20541. Numerous other markers extending as far as 1 Mb upstream of IL13 also show moderate LD with rs20541 (r²>0.2). Therefore, to more precisely define the causal variants in the 5q31 region, we carried out a comprehensive analysis of the genetic variation in this region using three large, independent sample sets, totaling 1,448 cases and 1,385 controls, and report findings from association tests of the markers with psoriasis risk.

Results

Single Marker Association Test Identifies Multiple SNPs Associated with Psoriasis

Examination of the 5q31 LD structure in the CEU HapMap Phase II dataset, led us to focus our fine-mapping to a 725 kb region that delimits the entire cytokine cluster, extending from ˜50 kb upstream of IL3 to ˜50 kb downstream of IL4 (FIG. 2). This region was targeted with 90 finemapping SNPs, primarily selected as tagging markers (see Materials and Methods). Allelic association tests of the 90 SNPs with psoriasis identified nine significant ones (P<0.05, FIG. 2 and Table 12) in a 370 kb region extending from SLC22A4 to KIF3A and including IRF1, IL5, IL13 and IL4; these nine SNPs were then genotyped in two other case control sample sets. Although the significance of these nine markers varied in the two follow-up sample sets (FIG. 2 and Table 13), all nine were significant in a combined analysis of the three sample sets (Mantel-Haenszel P_(combined)<0.05, Table 8 & Table 13). Breslow-Day tests provided no evidence for heterogeneity of effect across sample sets except for one marker, rs11568506 (odds ratio (OR) homogeneity: P_(rs11568506)=0.032; P>0.05 for all other markers). Based on the P_(comb)-values, none of the nine markers were more significant than rs1800925 (Mantel-Haenszel P_(combined)=0.00015), one of the three IL13 markers in our original report (Chang, M., Li, Y., Yan, C., Callis-Duffin, K. P., Matsunami, N., Garcia, V. E., Cargill, M., Civello, D., Bui, N., Catanese, J. J., Leppert, M. F. et al. (2008) Variants in the 5q31 cytokine gene cluster are associated with psoriasis. Genes Immun, doi:10.1038/sj.gene.6364451), although some were comparable (e.g. P=0.00022 for the IL4 marker rs2227282). Odds ratios conferred by these variants were all modest (Table 8 & Table 13).

Conditional Analysis Reveals Two Independent SNPs

To tease apart association signals from patterns of LD and determine which of these nine SNPs were independently associated with psoriasis risk, we performed pairwise tests of association for each SNP, using the combined datasets, conditioning on the genotypes at the other SNPs and evaluated the significance of these results using a permutation test (10,000 permutations) (Table 9). This approach is similar to the haplotype method (Valdes, A. M. and Thomson, G. (1997) Detecting disease-predisposing variants: the haplotype method. Am J Hum Genet, 60, 703-716) and has been previously described (Cargill, M., Schrodi, S. J., Chang, M., Garcia, V. E., Brandon, R., Callis, K. P., Matsunami, N., Ardlie, K. G., Civello, D., Catanese, J. J. et al. (2007) A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet, 80, 273-390). Not surprisingly, the significant association of various SNPs with psoriasis disappeared upon conditioning on other markers. Most notably, the significance of all other markers except rs11568506 (in SLC22A4; P=0.0105) was abolished upon conditioning on rs1800925. Conversely, association of rs1800925 remained strong (P=0.0002) upon conditioning on rs11568506. Together these observations suggest that rs11568506 and rs1800925 make independent contributions to psoriasis risk. The genotype correlation between rs11568506 and rs1800925 is very low (r²=0.006), while that between rs1800925 and the other 10 SNPs varied from 0.016 to 0.801 (Table 14).

Next we assessed whether SNPs in high LD with rs1800925 could be more significantly associated with disease. Genotype data were retrieved from the HapMap CEU dataset (release #22, phase II April 07), covering 1 Mb on each side of the IL13 gene, and the correlation (as determined by r² values) between rs1800925 and all other 1559 SNPs was calculated. A total of 32 markers were highly correlated with rs1800925 (r²>0.8) (Table 15), 30 of which were in absolute LD (r²=1) with one another. One of these 30 markers, rs2897443 (r² with rs1800925=0.897), which was genotyped in all three psoriasis sample sets (Table 13), was nearly 10-fold less significant than rs1800925 (P_(rs2897443)=0.001 vs P_(rs1800925)=0.00015). In addition, rs2897443 was no longer significant after conditioning on rs1800925 (P=0.2168) while, rs1800925 remained significant after conditioning on rs2897443 (P=0.042). The other two markers (rs2706370 and rs12187537) were not genotyped; however, they were highly correlated with rs2897443 (r²=0.94) and less so with rs1800925 (r²=0.829 and 0.817, respectively) making it unlikely that they were more significantly associated with disease than rs1800925.

Given that two markers, rs11568506 and rs1800925, show independent association with psoriasis, we carried out haplotype analyses using both markers in each of the three individual sample sets and in a combined analysis of all three sample sets. Although it's difficult to determine whether the two independent markers reside in the same LD block (FIG. 2B), the number of double heterozygotes was small (7 cases and 16 controls out of 2,800 samples; see Table 16); therefore phase could be unambiguously established in 99.2% of all individuals. Three haplotypes were observed, with the fourth being relatively rare (<0.001 in cases or controls) due to the low frequency of rs11568506. The two common haplotypes were significantly associated with psoriasis in two of the three sample sets; they were not significant in sample set 2 although the odds ratios were in the same direction as in the other two sample sets (Table 10). In a meta-analysis, both common haplotypes were significantly associated with psoriasis, and significance of the most common haplotype, GC, was markedly higher than any single SNP(P=5.67×10⁻⁶ for the haplotype vs. 1.5×10⁻⁴ for rs1800925). The observed effect sizes in the combined analysis of all sample sets were 1.37 for the predisposing haplotype GC (rs11568506-rs1800925) (frequency: 0.827 in cases vs 0.777 in controls) and 0.75 for the protective haplotype GT (frequency: 0.156 in cases vs 0.198 in controls).

Distinct SNPs are Associated with Psoriasis and Crohn's Disease

Previous studies have identified linkage of the 5q31 locus and/or association of specific variants in this region with several other diseases, including CD and asthma/allergic disorders (Heinzmann, A., Mao, X. Q., Akaiwa, M., Kreomer, R. T., Gao, P. S., Ohshima, K., Umeshita, R., Abe, Y., Braun, S., Yamashita, T. et al. (2000) Genetic variants of IL-13 signalling and human asthma and atopy. Hum Mol Genet, 9, 549-559; Howard, T. D., Whittaker, P. A., Zaiman, A. L., Koppelman, G. H., Xu, J., Hanley, M. T., Meyers, D. A., Postma, D. S, and Bleecker, E. R. (2001) Identification and association of polymorphisms in the interleukin-13 gene with asthma and atopy in a Dutch population. Am J Respir Cell Mol Biol, 25, 377-384; Postma, D. S., Bleecker, E. R., Amelung, P. J., Holroyd, K. J., Xu, J., Panhuysen, C. I., Meyers, D. A. and Levitt, R. C. (1995) Genetic susceptibility to asthma—bronchial hyperresponsiveness coinherited with a major gene for atopy. N Engl J Med, 333, 894-900). Because psoriasis and CD may share common genetic etiology (psoriasis is approximately five times more common in Crohn's disease patients than in controls (Lee, F. I., Bellary, S. V. and Francis, C. (1990) Increased occurrence of psoriasis in patients with Crohn's disease and their relatives. Am J Gastroenterol, 85, 962-963) and the same missense SNP in IL23R is associated with both diseases (Duerr, R. H., Taylor, K. D., Brant, S. R., Rioux, J. D., Silverberg, M. S., Daly, M. J., Steinhart, A. H., Abraham, C., Regueiro, M., Griffiths, A. et al. (2006) A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science, 314, 1461-1463; Cargill, M., Schrodi, S. J., Chang, M., Garcia, V. E., Brandon, R., Callis, K. P., Matsunami, N., Ardlie, K. G., Civello, D., Catanese, J. J. et al. (2007) A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet, 80, 273-390; Capon: F., Di Meglio, P., Szaub, J., Prescott, N. J., Dunster, C., Baumber, L., Timms, K., Gutin, A., Abkevic, V., Burden, A. D. et al. (2007) Sequence variants in the genes for the interleukin-23 receptor (IL23R) and its ligand (IL12B) confer protection against psoriasis. Hum Genet, 122, 201-206; Smith, R. L., Warren, R. B., Eyre, S., Ho, P., Ke, X., Young, H. S., Griffiths, C. E. and Worthington, J. (2007) Polymorphisms in the IL-12beta and IL-23R Genes Are Associated with Psoriasis of Early Onset in a UK Cohort. J Invest Dermatol. doi:10.1038/sj.jid.5701140; Nair, R. P., Ruether, A., Stuart, P. E., Jenisch, S., Tejasvi, T., Hiremagalore, R., Schreiber, S., Kabelitz, D., Lim, H. W., Voorhees, J. J. et al. (2008) Polymorphisms of the IL12B and IL23R Genes Are Associated with Psoriasis. J Invest Dermatol. doi:10.1038/sj.jid.5701255), we were also interested in testing our psoriasis sample sets for 5q31 SNPs showing strong association with CD. The WTCCC reported several highly significant SNPs in this region in their CD dataset (Wellcome Trust Case Control Consortium. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678). When selecting our set of 103 tagging SNPs, we specifically selected those SNPs, in groups of highly correlated markers, with evidence of association with CD in the WTCCC sample set. Of the 90 fine-mapping markers described above, five were identical to those showing strong association with CD (rs2285673, rs4540166, rs2522057, rs6596075 and rs10077785, all with P<5×10⁻⁵ in ˜2,000 cases and ˜3,000 controls (Wellcome Trust Case Control Consortium. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678) and genotyped in all three of our psoriasis sample sets. None were significant in sample set 1 (all P>0.05), but one SNP, rs2522057, was significantly associated with psoriasis in a meta-analyses of the three sample sets combined (P=0.022) (Table 11). The pairwise conditional association test, however, showed that this significant association could be accounted for by the IL13 marker, rs1800925 (P=0.60 for rs2522057 conditioned upon rs1800925, whereas rs1800925 remained significant when conditioned upon rs2522057, P=0.017). Assuming a type I error rate of 0.05 and the population allele frequency observed in the WTCCC study, our combined analysis had 90% power to detect an allelic effect equal to an OR of 1.25, similar to the reported OR of 1.37 for rs6596075, which tags the 5q31 CD risk haplotype (Wellcome Trust Case Control Consortium. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678). Thus variants contributing to CD risk in this genomic region, which map 5′ of the psoriasis-associated SNPs near the LOC441108-region, do not make an independent contribution to the etiology of psoriasis in our sample sets. In addition, although neither of the two independent psoriasis markers, rs 1800925 and rs 11568506, was tested in the WTCCC's CD sample set, four other markers in high LD with rs 1800925 (r²=0.9 for all) were tested but were not significant in their study. Therefore, association with CD and psoriasis in this region appears to be explained by different variants.

Discussion

Our analysis of the 5q31 cytokine cluster reveals that multiple SNPs are significantly associated with psoriasis risk; however, incorporating LD into our analysis identified two SNPs independently associated with psoriasis: rs1800925 in IL13 and rs11568506 in SLC22A4, also known as organic cation transporter 1 (OCTN1). The observed significance was only marginal for the SLC22A4 marker (Mantel-Haenszel P_(combined)=00.043). However, combining the genotype information for this marker with the more frequent IL13 marker, which is also more significant (Mantel-Haenszel P_(combined)=0.00015), resulted in two common psoriasis-associated haplotypes that were both highly significant. In particular, the major risk haplotype, with a control allele frequency of 0.777 increasing to 0.827 in cases, showed more pronounced association with psoriasis (P_(combined)=5.67×10⁻⁶, OR=1.37).

The biological interpretation of this observation (i.e., variants in two linked genes) is not straight-forward; however, both SLC22A4 and IL13 are putative genetic risk factors for other autoimmune or autoinflammatory disorders. Specifically, two-locus functional polymorphisms (haplotypes) in SLC22A4 and the adjacent SLC22A5 genes have been reported to be associated with CD (Peltekova, V. D., Wintle, R. F., Rubin, L. A., Amos, C. I., Huang, Q., Gu, X., Newman, B., Van Oene, M., Cescon, D., Greenberg, G. et al. (2004) Functional variants of OCTN cation transporter genes are associated with Crohn disease. Nat Genet, 36, 471-475), although definitive causal variants remain to be determined (Silverberg, M. S. (2006) OCTNs: will the real IBD5 gene please stand up? World J Gastroenterol, 12, 3678-3681; Silverberg, M. S., Duerr, R. H., Brant, S. R., Bromfield, G., Datta, L. W., Jani, N., Kane, S. V., Rotter, J. I., Philip Schumm, L., Hillary Steinhart, A., et al. (2007) Refined genomic localization and ethnic differences observed for the IBD5 association with Crohn's disease. Eur J Hum Genet, 15, 328-335). Variation in IL13, an immunoregulatory cytokine produced by activated T helper 2-type (Th2) cells, has been most prominently associated with asthma (Heinzmann, A., Mao, X. Q., Akaiwa, M., Kreomer, R. T., Gao, P. S., Ohshima, K., Umeshita, R., Abe, Y., Braun, S., Yamashita, T. et al. (2000) Genetic variants of IL-13 signalling and human asthma and atopy. Hum Mol Genet, 9, 549-559, as well as atopic dermatitis (Tsunemi, Y., Saeki, H., Nakamura, K., Sekiya, T., Hirai, K., Kakinuma, T., Fujita, H., Asano, N., Tanida, Y., Wakugawa, M. et al. (2002) Interleukin-13 gene polymorphism G4257A is associated with atopic dermatitis in Japanese patients. J Dermatol Sci, 30, 100-107). Thus both genes have strong a priori plausibility as risk factors of psoriasis, particularly as multiple autoimmune disorders have been associated with the same genetic variant (Gregersen, P. K., Lee, H. S., Batliwalla, F. and Begovich, A. B. (2006) PTPN22: setting thresholds for autoimmunity. Semin Immunol, 18, 214-223; Yamada, R. and Ymamoto, K. (2005) Recent findings on genes associated with inflammatory disease. Mutat Res, 573, 136-151). Strong biological evidence for the involvement of SLC22A4 or IL13 in the pathogenesis of psoriasis has, however, not yet been reported. Further experimentation should help determine whether these genes play a role in the disease process.

The two identified SNPs are located in an intron of SLC22A4 and near the 5′ end of IL13, respectively, with no predictive functional significance based on the current SNP annotation, although rs 11568506 is 43 bp from a POU2F1 transcription factor binding site, which exhibits a high degree of conservation across multiple placental mammals. In the absence of a validated role in gene regulation for these SNPs, and the presence of several other excellent biological candidate genes within this region as well as critical regulatory elements (Mohrs, M., Blankespoor, C. M., Wang, Z., Loots, G. G., Afzal V., Hadeiba, H., Shinkai, K., Rubin, E. M., and Locksley, R. M. (2001) Deletion of a coordinate regulator of type 2 cytokine expression in mice. Nat. Immunol. 2, 842-847), it is tempting to speculate that untested, causal genetic variant(s) lie on the particular haplotype defined by the two independent SNPs identified here. Although our comprehensive, HapMap-based analysis of the variation in this region provided no evidence for such a marker, we were unable to develop assays for tagging SNPS covering 13.6% of the variation. In addition, SNPs not available in the HapMap, such as for the most significant marker, rs 1800925, may explain these results. Similarly, other genetic variants (insertions, deletions, etc) which have been implicated in the etiology of various complex diseases, including psoriasis (see, for example, Hollox, E. J., Huffmeier, U., Zeeuwen, P. L., Palla, R., Lascorz, J., Rodijk-Olthuis, D., van de Kerkhof, P. C., Traupe, H., de Jongh, G., den Heijer, M. et al. (2008) Psoriasis is associated with increased beta-defensin genomic copy number. Nat Genet, 40, 23-25, and Fanciulli, M., Norsworthy, P. J., Petretto, E., Dong, R., Harper, L., Kamesh, L., Heward, J. M., Gough, S. C., de Smith, A., Blakemore, A. I. et al. (2007) FCGR3B copy number variation is associated with susceptibility to systemic, but not organ-specific, autoimmunity. Nat Genet, 39, 721-723), may be responsible for modulating disease susceptibility.

Although the recent identification of IL23R variants associated with psoriasis and CD solidifies the notion that genetic etiology may overlap between these two diseases (Duerr, R. H., Taylor, K. D., Brant, S. R., Rioux, J. D., Silverberg, M. S., Daly, M. J., Steinhart, A. H., Abraham, C., Regueiro, M., Griffiths, A. et al. (2006) A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science, 314, 1461-1463; Cargill, M., Schrodi, S. J., Chang, M., Garcia, V. E., Brandon, R., Callis, K. P., Matsunami, N., Ardlie, K. G., Civello, D., Catanese, J. J. et al. (2007) A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet, 80, 273-390), our data suggest that this is unlikely to be the case for the 5q31 locus, where linkage signals have been detected for both diseases (Rioux, J. D., Silverberg, M. S., Daly, M. J., Steinhart, A. H., McLeod, R. S., Griffiths, A.M., Green, T., Brettin, T. S., Stone, V., Bull, S. B. et al. (2000) Genomewide search in Canadian families with inflammatory bowel disease reveals two novel susceptibility loci. Am J Hum Genet, 66, 1863-1870; Rioux, J. D., Daly, M. J., Silverberg, M. S., Lindblad, K., Steinhart, H., Cohen, Z., Delmonte, T., Kocher, K., Miller, K., Guschwan, S. et al. (2001) Genetic variation in the 5q31 cytokine gene cluster confers susceptibility to Crohn disease. Nat Genet, 29, 223-228; Samuelsson, L., Enlund, F., Torinsson, A., Yhr, M., Inerot, A., Enerback, C., Wahlstrom, J., Swanbeck, G. and Martinsson, T. (1999) A genome-wide search for genes predisposing to familial psoriasis by using a stratification approach. Hum Genet, 105, 523-529). Several previously reported CD-associated markers in the 5q31 region, including one that tags a risk haplotype (rs6596075), were not significant in our psoriasis sample sets and rs2522057, which showed modest association the combined analysis, could be explained by LD with the most significant marker, rs1800925. This is despite the fact that our sample sets had sufficient power to detect an effect size similar to that of the same markers observed in CD or the majority of the SNPs recently identified for other complex diseases (Wellcome Trust Case Control Consortium. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678). This observation is consistent with a recent report that failed to detect a significant association between psoriasis and three other CD-associated SNPs in SLC22A4 and SLC22A5 (Friberg, C., Bjorck, K., Nilsson, S., Inerot, A., Wahlstrom, J. and Samuelsson, L. (2006) Analysis of chromosome 5q31-32 and psoriasis: confirmation of a susceptibility locus but no association with SNPs within SLC22A4 and SLC22A5. J Invest Dermatol, 126, 998-1002). It is possible that the lack of association may be partly confounded by the fact that the actual causal CD variants have not been tested. However, judging from the significance level observed in the WTCCC study (Wellcome Trust Case Control Consortium. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678), and unless there are significant differences in the LD structure between their sample sets and ours (which is unlikely), we favor the interpretation that susceptibility to psoriasis and CD are affected by distinct genetic variants at this locus or the same variants but with different effect sizes.

Finally, it is possible that we may not have the power to detect conditional association effects in our sample sets. Thus, although the significance of all other markers except rs11568506 can be accounted for by rs1800925, we cannot exclude the possibility that these other markers, particularly the stronger ones in IL13 and IL4, make additional, independent contributions to disease risk. In addition, the possibility that our current finding is false positive cannot be completely discounted, since the observed significance will not survive multiple-testing corrections if all markers tested in our previous genome-wide scan and in this study are considered—which is generally deemed conservative. However, in an unpublished GWA study of ˜1400 Caucasian cases and ˜1400 Caucasian controls, two of our original IL13-associated SNPs, rs20541 and rs848, exhibit very strong evidence for association. These data suggest markers in the 5q31 region are true psoriasis risk markers and suggest that variants in both the IL13 and SLC22A4 regions independently contribute to psoriasis risk. Validation in other sample sets of similar characteristics is warranted to bring further definition to the causal variant(s).

Materials and Methods Clinical Samples

Three sample sets of psoriasis cases and unaffected controls were assembled in this study. Detailed demographics and diagnostic/enrollment criteria have been described in a previous publication (Cargill, M., Schrodi, S. J., Chang, M., Garcia, V. E., Brandon, R., Callis, K. P., Matsunami, N., Ardlie, K. G., Civello, D., Catanese, J. J. et al. (2007) A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet, 80, 273-390). Briefly, all individuals are North American whites of European descent and were 18 years or older when they contributed their samples (sample set 1: 467 cases/460 controls; sample set 2: 498 cases/498 controls; sample set 3: 483 cases/427 controls. Informed written consent was obtained from all participants, and all protocols were approved by national and/or local institutional review board.

Selection of Tagging SNPs and Overall Study Design

To comprehensively yet efficiently investigate the 5q31 region, we ran the MIT tagger program (http://www.broad.mit.edu/mpg/tagger/server.html) (de Bakker, P. I., Yelensky, R., Pe'er, I., Gabriel, S. B., Daly, M. J. and Altshuler, D. (2005) Efficiency and power in genetic association studies. Nat Genet, 37, 1217-1223) with the CEU HapMap phase II dataset (The landmarks were set at chr5:131373131..132097638 [NCBI Build 36]; minimal allele frequency was set at 0.02; r² threshold was set at 0.8; and tagging mode was set at pair wise). A total of 103 tagging SNPs (including the previously tested IL13 marker rs20541) are required to capture the SNP diversity (610 polymorphic sites) within this 725 kbp region. Assay designs were attempted on the default tagging markers, and when not feasible, on an alternative marker within the tagging group. We also referenced the WTCCC study to preferentially select markers that were significantly associated with CD as tagging markers. We were able to develop assays for 90 of these tagging SNPs (excluding the assay already available for rs20541). In addition, assays for two SNPs in predicted transcription factor binding sites (rs381870 and rs4540166) were developed.

The above 92 SNPs were genotyped in one of the three psoriasis case-control sample sets (sample set 1; 467 cases/460 controls). Genotyping data for 90 markers, including 88 tagging markers (equivalent to 86.4% coverage for all tagging markers), passed quality control and were subjected to further analysis. Hardy-Weinberg equilibrium tests identified six markers in controls and two others in cases with P<0.05 (Table 12). However, analysis of the data showed no apparent error in genotype assignment for these markers, thus they were included in the association tests. As predicted, inter-marker LD between the genotyped markers was low in our samples (FIG. 2). Significant SNPs (P<0.05) were then genotyped in two other case control sample sets (sample set 2 (498 cases/498 controls) and sample set 3 (481 cases/424 controls)).

Genotyping

Genotyping was carried out by the method of real-time, allele-specific PCR with primers designed and validated in-house (Germer, S., Holland, M. J. and Higuchi, R. (2000) High-throughput SNP allele-frequency determination in pooled DNA samples by kinetic PCR. Genome Res, 10, 258-266). Genotype calls of cases and controls, randomly arrayed onto genotyping plates, were made using an automated algorithm and were subjected to manual examination without knowledge of case-control status, prior to statistical analysis. Our genotyping accuracy has been consistently better than 99% (Cargill, M., Schrodi, S. J., Chang, M., Garcia, V. E., Brandon, R., Callis, K. P., Matsunami, N., Ardlie, K. G., Civello, D., Catanese, J. J. et al. (2007) A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet, 80, 273-390; Li, Y., Rowland, C., Catanese, J., Morris, J. C., Lovestone, S., O'Donovan M, C., Goate, A., Owen, M., Williams, J. and Grupe, A. (2008) SORL1 variants and risk of late-onset Alzheimer's disease. Neurobiol Dis, 29, 293-296).

Statistical Analysis

Hardy-Weinberg equilibrium was examined by an exact test. Allelic association of SNPs with psoriasis risk was determined by the χ² test in individual sample sets and by meta-analysis using fixed effects of Mantel-Haenszel methods to combine odds ratios across the sample sets. Marker conditional association tests were done using an approach similar to the haplotype method (22) as previously described (Cargill, M., Schrodi, S. J., Chang, M., Garcia, V. E., Brandon, R., Callis, K. P., Matsunami, N., Ardlie, K. G., Civello, D., Catanese, J. J. et al. (2007) A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet, 80, 273-390). In brief, cases and controls were partitioned on the basis of genotypes at one SNP, and counts at an interrogated SNP were subjected to analysis where statistical significance was assessed by a permutation method (10,000 permutations). For marker-marker LD measurements, D′ and r² were calculated using LDMax (www.sph.umich.edu/csg/abecasis/GOLD/docs/ldmax.html). Haplotypes were estimated and tested for association with disease status using a score test with haplotypes coded in an additive fashion (Schaid, D. J., Rowland, C. M., Tines, D. E., Jacobson, R. M. and Poland, G. A. (2002) Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet, 70, 425-434). Power calculations were done using the PS:Power and Sample Size Calculation Program, obtained from the Department of Biostatistics, Vanderbilt University website (http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize).

Example Three Additional LD SNPs Associated with Psoriasis

Additional investigation was conducted to identify SNPs in linkage disequilibrium (LD) with certain “interrogated SNPs” which have been found to be associated with psoriasis, as shown in the tables. The interrogated SNPs, which are shown in column 1 (which indicates the hCV identification numbers of each interrogated SNP) and column 2 (which indicates the public rs identification numbers of each interrogated SNP) of Table 4. The methodology is described earlier in the instant application. To summarize briefly, the power threshold (T) was set at an appropriate level, such as 51%, for detecting disease association using LD markers. This power threshold is based on equation (31) above, which incorporates allele frequency data from previous disease association studies, the predicted error rate for not detecting truly disease-associated markers, and a significance level of 0.05. Using this power calculation and the sample size, for each interrogated SNP a threshold level of LD, or r² value, was derived (r_(T) ², equations (32) and (33) above). The threshold value r_(T) ² is the minimum value of linkage disequilibrium between the interrogated SNP and its LD SNPs possible such that the non-interrogated SNP still retains a power greater or equal to T for detecting disease-association.

Based on the above methodology, LD SNPs were found for the interrogated SNPs. Several exemplary LD SNPs for the interrogated SNPs are listed in Table 4; each LD SNP is associated with its respective interrogated SNP. Also shown are the public SNP IDs (rs numbers) for the interrogated and LD SNPs, when available, and the threshold r² value and the power used to determine this, and the r² value of linkage disequilibrium between the interrogated SNP and its matching LD SNP. As an example in Table 4, the interrogated, psoriasis-associated SNP rs20541 (hCV2259921) was calculated to be in LD with rs847 (hCV8932046) at an r² value of 0.9061, based on a 51% power calculation, thus establishing the latter as a marker associated with psoriasis as well.

All publications and patents cited in this specification are herein incorporated by reference in their entirety. Various modifications and variations of the described compositions, methods and systems of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments and certain working examples, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the above-described modes for carrying out the invention that are obvious to those skilled in the field of molecular biology, genetics and related fields are intended to be within the scope of the following claims.

TABLE 1 Transcript SNP info and associated gene/protein information Gene Number: 1 Gene Symbol IL13-3596 Gene Name: interleukin 13 Transcript Accession: NM_002188 Protein Accession: NP_002179 Chromosome: 5 OMIM NUMBER: 147683 OMIM Information: {Asthma, susceptibility to}, 600807 (3); {Allergic rhinitis,/susceptibility to}, 607154 (3) Transcript Sequence (SEQ ID NO: 1): Protein Sequence (SEQ ID NO: 3): SNP Information Context (SEQ ID NO: 5): GGCAGTTTTCCAGCTTGCATGTCCGAGACACCAAAATCGAGGTGGCCCAGTTTGTAAAG GACCTGCTCTTACATTTAAAGAAACTTTTTCGCGAGGGACA R TTCAACTGAAACTTCGAAAGCATCATTATTTGCAGAGACAGGACCTGACTATTGAAGTT GCAGATTCATTTTTCTTTCTGATGTCAAAAATGTCTTGGGT Celera SNP ID: hCV2259921 Public SNP ID: rs20541 SNP Chromosome Position: 132023863 SNP in Transcript Sequence SEQ ID NO: 1 SNP Position Transcript: 446 SNP Source: Applera Population(Allele, Count): Caucasian (A,61G,34) African American (A,71G,29) total (A,131G,63) SNP Type: Missense Mutation Protein Coding: SEQ ID NO: 3, at position 144,(Q,CAG) (R,CGG) SNP Source: HGMD; dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (A,281G,92) SNP Type: Missense Mutation Protein Coding: SEQ ID NO: 3, at position 144,(Q,CAG) (R,CGG) Context (SEQ ID NO: 6): GAGTGTGTTTGTCACCGTTGGGGATTGGGGAAGACTGTGGCTGCTAGCACTTGGAGCCA AGGGTTCAGAGACTCAGGGCCCCAGCACTAAAGCAGTGGAC M CCAGGAGTCCCTGGTAATAAGTACTGTGTACAGAATTCTGCTACCTCACTGGGGTCCTG GGGCCTCGGAGCCTCATCCGAGGCAGGGTCAGGAGAGGGGC Celera SNP ID: hCV8932051 Public SNP ID: rs848 SNP Chromosome Position: 132024399 SNP in Transcript Sequence SEQ ID NO: 1 SNP Position Transcript: 981 SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (A,291C,91) SNP Type: UTR3 Context (SEQ ID NO: 7): AGCCTCATCCGAGGCAGGGTCAGGAGAGGGGCAGAACAGCCGCTCCTGTCTGCCAGCC AGCAGCCAGCTCTCAGCCAACGAGTAATTTATTGTTTTTCCT Y GTATTTAAATATTAAATATGTTAGCAAAGAGTTAATATATAGAAGGGTACCTTGAACAC TGGGGGAGGGGACATTGAACAAGTTGTTTCATTGACTATCA Celera SNP ID: hCV8932O46 Public SNP ID: rs847 SNP Chromosome Position: 132024568 SNP in Transcript Sequence SEQ ID NO: 1 SNP Position Transcript: 1150 Related Interrogated SNP: hCV2259921 (Power = .51) Related Interrogated SNP: hCV8932051 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (T,271C,91) SNP Type: UTR3 Context (SEQ ID NO: 8): AGCAGCTCAGGCACACTTCTTCTTGGTCTTATTTATTATTGTGTGTTATTTAAATGAGTG TGTTTGTCACCGTTGGGGATTGGGGAAGACTGTGGCTGCT R GCACTTGGAGCCAAGGGTTCAGAGACTCAGGGCCCCAGCACTAAAGCAGTGGACACCA GGAGTCCCTGGTAATAAGTACTGTGTACAGAATTCTGCTACC Celera SNP ID: hCV8932052 Public SNP ID: rs1295685 SNP Chromosome Position: 132024344 SNP in Transcript Sequence SEQ ID NO: 1 SNP Position Transcript: 926 Related Interrogated SNP: hCV2259921 (Power = 0.51) Related Interrogated SNP: hCV8932051 (Power = 0.51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (A,291G,91) SNP Type: UTR3 Gene Number: 2 Gene Symbol KIF3A-11127 Gene Name: kinesin family member 3A Transcript Accession: NM_007054 Protein Accession: NP_008985 Chromosome: 5 OMIM NUMBER: 604683 OMIM Information: Transcript Sequence (SEQ ID NO: 2): Protein Sequence (SEQ ID NO: 4): SNP Information Context (SEQ ID NO: 9): TTGAGACTTTATAAGTTAGTATTTATTAAATTAATATTATTTTAATAAGTTTTGTTAAATC CTAGTTTAAATGACAAAGCTGTGTATAGAGTAGGGGTGA S TGAAGGTGGAGTACTCTTGAGGTGGCGATAAAAATGTACCCATAAATTTAAAGTCCATT CTGTAATTGGAATACTGAATGTCCTGTGTGTGGTGAACATT Celera SNP ID: hCV11818506 Public SNP ID: rs17690965 SNP Chromosome Position: 132058566 SNP in Transcript Sequence SEQ ID NO: 2 SNP Position Transcript: 3913 Related Interrogated SNP: hCV16176374 (Power = 0.6) SNP Source: dbSNP; Celera; HapMap Population(Allele, Count): Gaucasian (G,311C,69) SNP Type: UTR3

TABLE 2 Genomic SNP info and associated gene information Gene Number: 1 Gene Symbol: IL13-3596 Gene Name: interleukin 13 Chromosome: 5 OMIM NUMBER: 147683 OMIM Information: {Asthma, susceptibility to}, 600807 (3); {Allergic rhinitis,/susceptibility to}, 607154 (3) Genomic Sequence (SEQ ID NO: 10): SNP Information Context (SEQ ID NO: 19): CAGCAGTTTTCCAGCTTGCATGTCCGAGACACCAAAATCGAGGTGGCCCAGTTTGTAAA GGACCTGCTCTTACATTTAAAGAAACTTTTTCGCGAGGGAC R GTTCAACTGAAACTTCGAAAGCATCATTATTTGCAGAGACAGGACCTGACTATTGAAGT TGCAGATTCATTTTTCTTTCTGATGTCAAAAATGTCTTGGG Celera SNP ID: hCV2259921 Public SNP ID: rs20541 SNP Chromosome Position: 132023863 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 12099 SNP Source: Applera Population(Allele, Count): Caucasian (A,6|G,34) African American (A,7|G,29) total (A,13|G,63) SNP Type: MISSENSE MUTATION; ESE; UTR3; SILENT RARE CODON; SILENT MUTATION SNP Source: HGMD, dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (A,28|G,92) SNP Type: MISSENSE MUTATION; ESE; UTR3; SILENT RARE CODON; SILENT MUTATION Context (SEQ ID NO: 20): TATTCTAAATACTTGAAAACTTTAAATGTATTCATTCCTCAGAGCAACTTCATGAGACAG GGACAGCTATGACCCCTATTTCACAGATGAGGCTGAGTAG M GTGCCCAAGGTCACACAGCCAGGAGGCACAGCAGCCAGGCCTGACAGACCACCTGGGC CCAGCGTCCGCTCTCTTAGCCACCGTGTACTATAGCAGCCTC Celera SNP ID: hCV2259917 Public SNP ID: rs762534 SNP Chromosome Position: 132032655 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 20891 SNP Source: dbSNP; Celera; HapMap; ABI_VaV HGBASE Population(Allele, Count): Caucasian (C,111|A,9) SNP Type: INTERGENIC; UNKNOWN Context (SEQ ID NO: 21): GAGTGTGTTTGTCACCGTTGGGGATTGGGGAAGACTGTGGCTGCTAGCACTTGGAGCCA AGGGTTCAGAGACTCAGGGCCCCAGCACTAAAGCAGTGGAC M CCAGGAGTCCCTGGTAATAAGTACTGTGTACAGAATTCTGCTACCTCACTGGGGTCCTG GGGCCTCGGAGCCTCATCCGAGGCAGGGTCAGGAGAGGGGC Celera SNP ID: hCV8932051 Public SNP ID: rs848 SNP Chromosome Position: 132024399 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 12635 SNP Source: dbSNP, Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (A,29|C,91) SNP Type: UTR3 Context (SEQ ID NO: 22): TCGGGGAGGAAGTGGGTAGGGGAGAAATCTTGACATCAACACCCAACAGGCAAATGCC GTGGCCTCTGCTGTGGGGGTTTCTGGAGGACTTCTAGGAAAA Y GAGGGAAGAGCAGGAAAAGGCGACATGGCTGCAGGGGCCAAGCCCAGGAGCCGCCCT CCACAGCACTCATTCTGCAGAAGGGAAATTTGAGGCCCCCAGA Celera SNP ID: hCV50000065 Public SNP ID: rs1800925 SNP Chromosome Position: 132020708 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 8944 SNP Source: dbSNP; HGBASE Population(Allele, Count): Caucasian (C,97|T,23) SNP Type: MISSENSE MUTATION; UTR5; INTRON Context (SEQ ID NO: 23): CACAGCAGGGAGAGTGCTGTGTTATGCGAGGAGGTTGGAGAAATCCTCCCCATGAGAT AAGATGGGAACAGAGATCGGGACGAAACAAGGGGAGGGGACA M CCATGCACAGATCGGGGCAAAGTCATCAAGGGAAAGGGAACTGCAGGTGCTAAGGTCT TGAGCAAGAGCGAGCTGGGGATGCCCTTCCCAGCACTCACGG Celera SNP ID: hCV16176063 Public SNP ID: rs2243211 SNP Chromosome Position: 132029321 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 17557 SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (C,108|A,12) SNP Type: TFBS SYNONYMOUS; INTERGENIC; UNKNOWN Context (SEQ ID NO: 24): CAGTACCCACCTCATGGGGACTTCCGTGAGGACTGAATGAGACAGTCCCTGGAAAGCC Y GTGCTGACCTCTTTGTCCTGCAGCAGTTTTCCAGCTTGCATGTCCGAGACACCAAAATCG AGGTGGCCCAGTTTGTAAAGGACCTGCTCTTACATTTAAA Celera SNP ID: hCV8932053 Public SNP ID: rs1295686 SNP Chromosome Position: 132023742 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 11978 Related Interrogated SNP: hCV2259921 (Power = 0.51) Related Interrogated SNP: hCV8932O51 (Power = 0.51) SNP Source: Applera Population(Allele, Count): Caucasian (C,3|1T,7) African American (C,13|1T,23) total (C,46|T,30) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON SNP Source: dbSNP; HapMap; ABI_Val; HGBASE Population(Allele, Count): Caucasian (T,28|C,92) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 25): AGCCTCATCCGAGGCAGGGTCAGGAGAGGGGCAGAACAGCCGCTCCTGTCTGCCAGCC AGCAGCCAGCTCTCAGCCAACGAGTAATTTATTGTTTTTCCT Y GTATTTAAATATTAAATATGTTAGCAAAGAGTTAATATATAGAAGGGTACCTTGAACAC TGGGGGAGGGGACATTGAACAAGTTGTTTCATTGACTATCA Celera SNP ID: hCV8932046 Public SNP ID: rs847 SNP Chromosome Position: 132024568 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 12804 Related Interrogated SNP: hCV2259921 (Power = .51) Related Interrogated SNP: hCV8932051 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (T,27|C,91) SNP Type: MICRORNA;UTR3 Context (SEQ ID NO: 26): AGCAGCTCAGGCACACTTCTTCTTGGTCTTATTTATTATTGTGTGTTATTTAAATGAGTG TGTTTGTCACCGTTGGGGATTGGGGAAGACTGTGGCTGCT R GCACTTGGAGCCAAGGGTTCAGAGACTCAGGGCCCCAGCACTAAAGCAGTGGACACCA GGAGTCCCTGGTAATAAGTACTGTGTACAGAATTCTGCTACC Celera SNP ID: hCV8932052 Public SNP ID: rs1295685 SNP Chromosome Position: 132024344 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 12580 Related Interrogated SNP: hCV2259921 (Power = .51) Related Interrogated SNP: hCV8932051 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (A,29|G,91) SNP Type: MICRORNA; UTR3 Context (SEQ ID NO: 27): GAGGTGTTATAGTGCCAGGGAAAAGGGGTGCAAGACTAGTGTTTGCCTCTTATTTCTTG TCTTGTTTATAAATTCTCTACATTAAAAATATAAATTCCAA R TGAAAACAATGGAATAAACACAAACTCCCACTTTATAACCAACTAAAATGACAGTAAC TGAATTTTCTAAAGTTTTAAACCCACCAAAATGGGAGAAATG Celera SNP ID: hCV27452177 Public SNP ID: rs3091307 SNP Chromosome Position: 132017035 SNP in Genomic Sequence: SEQ ID NO: 10 SNP Position Genomic: 5271 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_VaL HGBASE Population(Allele, Count): Caucasian (A,94|G,26) SNP Type: INTRON Gene Number: 2 Gene Symbol: KIF3A-11127 Gene Name: kinesin family member 3A Chromosome: 5 OMIM NUMBER: 604683 OMIM Information: Genomic Sequence (SEQ ID NO: 11): SNP Information Context (SEQ ID NO: 28): AATAGGCTTTAACTCTTTATGACAAAATTATGTAAAAAACTCTAGGGTTTAAAATACAT TATTTGTATAGAACTGTAAGTCCAGAGCAAACCTAAGACAC R AGACAAATGAAATAAAAGAGTAAATAAAAGCACTGATCCAAATTCATGCTTTGGTGTG GTGTGATTCATTGCATTCCAAAAGTCAGGATGTAGACTGATT Celera SNP ID: hCV29575948 Public SNP ID: rs10069772 SNP Chromosome Position: 132068587 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 22365 SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (A,16|G,104) SNP Type: INTRON Context (SEQ ID NO: 29): ACAGTTGCTGAGCTGTCAAGATATATCATCATACACTAAAGGAACAACTAAAATAGCTT GTTATTATGTCAAACTCAGGACCCGAAGGCTCCCTAAGTCA R AGTAAACATGCCAAGTGTCAAATATAAGTAGAAATACTACCAAATAATAAAGCTGGAA TATGGCTTCCAATTGATTATTTTTGCTAATAATGTCTAATAA Celera SNP ID: hCV31237680 Public SNP ID: rs12186803 SNP Chromosome Position: 132067968 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 21746 SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (G,101|A,19) SNP Type: TRANSCRIPTION FACTOR BINDING SITE;INTRON Context (SEQ ID NO: 30): TCACCCTCTTCATCATCATCTTCCTCTGACCCACTGATATCAGAGCCTGATATTTCTTCTC CTTGGACAGAAATTTAATACAATGTTTTTTATCCTGTAC S AAGTCATTAGCTATTTAGGAAATACCTATATCAGCTTTCTTCCTCAGATTTTTGTTACTG CAAACAACAAATTTAAATACTATTTAAATGCTCCTAGAAA Celera SNP ID: hCV11818501 Public SNP ID: rs41564665 SNP Chromosome Position: 132074688 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 28466 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: Applera Population(Allele, Count): Caucasian (C,30|G,10) African American (C,14|G,20) total C,44|G,30) SNP Type: INTRON SNP Source: dbSNP; Celera; HapMap Population(Allele, Count): Caucasian (G,34|C,86) SNP Type: INTRON Context (SEQ ID NO: 31): AAGGGTGTTCTAAAGCTGGATTGTGATGATGGTTTCACAACTGTATATATTTACTACACT CATCAAACTGCACAGCTGCAATATGTGAATTTATGGCACA K TAATTATGTCTCAATAAGGGAGTTTTTTAAAAAGTAAAGATTCACTTACCATTGTAGCCT TCAAGTACAGAATCAATAATAGGTCTTGCAGTTAAGTTAT Celera SNP ID: hCV2558119 Public SNP ID: rs9784600 SNP Chromosome Position: 132097746 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 51524 Related Interrogated SNP: hCV16176374 (Power = .51) SNP Source: dbSNP; Celera Population(Allele, Count): Caucasian (G,95|T,21) SNP Type: INTRON Context (SEQ ID NO: 32): ATTAATGGAAGAGAGTTCTGAAATAAATATTTATGGTAAAAAGGTTTGAAATGTAGGCC ATTATAATATGTCCATTTCATTTGCATATTATCTATATTCT K ATCATAATAAACAGTCTTAATTCACACGTGGCAACAAATCCCCCACAACAGAGTGAGA ATGAGAATACATTTTATAAAGAAAAAGAACTAAATTTAAAAA Celera.SNP ID: hCV2558124 Public SNP ID: rs10036532 SNP Chromosome Position: 132073714 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 27492 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; Celera; HapMap Population(Allele, Count): Caucasian (G,33|T,81) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 33): TTTCCTTTTAAAAGGGTGAAAGATAGCCTATTTTTAATATTTAATAACATCTAAAATATT GAAGTCATAAAACTAAAATCTTAATAACATTGAGTAATAA W AGTCTCAAAACATTATATACTGATATAACCTTTTTATAAATTCTAAAATACATATTTCTT AGGAATGCATGCAGATGAAATAAAACTAAATGAAAATGAA Celera SNP ID: hCV8932022 Public SNP ID: rs1468215 SNP Chromosome Position: 132064388 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 18166 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (T,34|A,86) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 34): TCTGAGGTACTCAAAAGGTGCAAGTGGATTCAGAGGAAATCAGAAACAAATCATCTCA GGCAATACCTGATGCATATGTAGGGACTTATGGAAATGCTAC R TTAATGTATTATATTTATTGCAATTTTGCCTAGCCCATCTAACTATGTGTTTCTAACAAG GAACATCACATTGAACATATCATATAATGTAACATAGCAT Celera SNP ID: hCV11740439 Public SNP ID: rs2023823 SNP Chromosome Position: 132060705 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 14483 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (A,34|G,82) SNP Type: INTRON Context (SEQ ID NO: 35): TTAAAAATTATTATTTTAATGCTATTGCCTACTTTTTTATTGTTGATATGATCAACTCCAT TAACTAGATTATAAAAAGAAATGTCATAAGAGCAAAAAT R AACTGAAATGTATTAACAACAGTATTATCATAGAATCCAGCAATTCCATTGCTGGGTAC ATACCCCAAAAATGGTAAAACAAGAATTCAAACAGGTATTA Celera SNP ID: hCV11818497 Public SNP ID: rs4266392 SNP Chromosome Position: 132077154 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 30932 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (G,35|A,85) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 36): AAGGAAAACTGGCCAAATTTGAATAAAGTCCACAGATTAGTTAATAAGACTATATCAAT GTAAATCTCCTGGTTTCAATAACTGTTGTATGCTTATAAAA R ATGTTAACATGAGGTGAAGCTGGGTGAAGGGTAGATAGGAACTCTCCATATCATGTTTG CAAGTTGCATATAGCCTACAATAATTTTTAAGTTTAAAAAT Celera SNP ID: hCV11818498 Public SNP ID: rs4425499 SNP Chromosome Position: 132076961 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 30739 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (A,34|G,86) SNP Type: INTRON Context (SEQ ID NO: 37): GGATTGTATCCTGGACCAGGAAAAAGAGGCACTGGAAGGAAAACTGGCCAAATTTGAA TAAAGTCCACAGATTAGTTAATAAGACTATATCAATGTAAAT Y TCCTGGTTTCAATAACTGTTGTATGCTTATAAAAAATGTTAACATGAGGTGAAGCTGGG TGAAGGGTAGATAGGAACTCTCCATATCATGTTTGCAAGTT Celera SNP ID: hCV11818499 Public SNP ID: rs2897442 SNP Chromosome Position: 132076926 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 30704 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (C,35|T,85) SNP Type: INTRON Context (SEQ ID NO: 38): AATGTTCACCACACACAGGACATTCAGTATTCCAATTACAGAATGGACTTTAAATTTAT GGGTACATTTTTATCGCCACCTCAAGAGTACTCCACCTTCA S TCACCCCTACTCTATACACAGCTTTGTCATTTAAACTAGGATTTAACAAAACTTATTAAA ATAATATTAATTTAATAAATACTAACTTATAAAGTCTCAA Celera SNP ID: hCV11818506 Public SNP ID: rs17690965 SNP Chromosome Position: 132058566 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 12344 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; Celera; HapMap Population(Allele, Count): Caucasian (C,31|G,69) SNP Type: MICRORNA; UTR3; INTRON Context (SEQ ID NO: 39): TAGATTACAGTAATTTCCACTGATTATTTGATTAACAGAAAGGTCTAATGAAGAATTTTT AAAGACACAAGTTCTCCCAATACATCCGATTTCCCAACCG K ATTAAGGGCTTTTTTTTTTTTCCAGAAAATAAAATTATTACATAAAATGAAGTATACCTG TACAGTAACAAACTTACATTCCTTCTATTAGTAGATTTTT Celera SNP ID: hCV15751509 Public SNP ID: rs2299009 SNP Chromosome Position: 132070712 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 24490 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (G,35|T,85) SNP Type: INTRON Context (SEQ ID NO: 40): GGCCAGGATGGTCTCAATCTCTTGACCTCGTGATCCACCTGCCTCAACCTCCCAAAGTG CTGGGATTACAGGGGTGAGCCACCACACCCGGCCCAGCAGT W GTTTTTCTAAAACACAAATAGGCCAGGTGCAGTGGCTCACGCCTGTAATCCCGGCACTT TGGGAGACTGAGATGGGAGCTTCGCTTGAACCCAAAAGTTC Celera SNP ID: hCV15793498 Public SNP ID: rs2406539 SNP Chromosome Position: 132051175 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 4953 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (A,29|T,77) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTERGENIC; UNKNOWN Context (SEQ ID NO: 41): AGAGAATAGCAGCCACTCACTTTAGACAGGACTGCTCACCACTGCCAATTTTTCTCCTA CGCTGGGGACAAATGTCTTACCATTTATCATCACACTTGCA Y TGTCATTTTATTAAAAATGGAAAAAAAGATACATCTAAAGGGACTCATGTTTCTTAGAA CTGATTTGCTTCACTACAATATGCTTGGCATGTGTTGGTAT Celera SNP ID: hCV15955623 Public SNP ID: rs2237059 SNP Chromosome Position: 132061511 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 15289 Related Interrogated SNP: hCV16176374 (Power = .51) SNP Source: dbSNP; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (C,96|T,20) SNP Type: INTRON Context (SEQ ID NO: 42): ACCAAGAGACAGCCGTATGAGAGTTGCATAGATTGGCGCTACTAAAAAGAAACAAAAT TTTGCTCGGCATGGAAAATGATTTGTGGTATCTTTCATTCAT M TATGCTCTCTACATGGTAAATAATCAAAGTGGAATGGTTTTCAAGAAACCTAAAAAATC CAGTCTCTACAGGGTAAAGTGCCATACCAAATTTGCTTCTA Celera SNP ID: hCV16164369 Public SNP ID: rs41564561 SNP Chromosome Position: 132084046 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 37824 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; HapMap; ABI_VaP HGBASE Population(Allele, Count): Caucasian (C,35|A,85) SNP Type: INTRON Context (SEQ ID NO: 43): AAAATAGCTACACTGGAGACAAAAATCCAAGCTTTTAATTACTATTCAATTTTCCTATTT TATATATCCTAGGAAAAAACAGAAATTTAAATCATGAATT M ATCTCAATTCAATAATACTCACTGGGCTTTAAGAAGATCTTTTTCCCGTTTCTCTAATTC AGCTCTAGCCTTGTTTCTTTCTTCTTCTTCCATGTCGAGC Celera SNP ID: hCV16177653 Public SNP ID: rs2285700 SNP Chromosome Position: 132067031 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 20809 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; HapMap; ABI_VaP HGBASE Population(Allele, Count): Caucasian (C,34|A,86) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 44): ACTGGGTTTCTAAATACCATTCTCCTACAGAAGGGACCAGAGAGAAGTGAATGATTCTA AGATTGGGCAAGGAAGACACAAGATAAACCTAAAACCTTTT R TAGTGCCAAAAAGTAATCAAATGCTCAAAAAAACTGATGAGCACATGCTGAAAGAATA CAGGAACTACTCTGAAGGATCTCATAATAGCCAAAGCTGGAA Celera SNP ID: hCV26478846 Public SNP ID: rs6864565 SNP Chromosome Position: 132075870 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 29648 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; Celera; HapMap Population(Allele, Count): Caucasian (G,34|A,86) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 45): CAGAGTCTTGGGAGAAGAAATTTCTGAAAAATATTTTTTTCTTCGGGCTTAGAAACAAT GACATCTTGGTAGTAATGAGCACAGCTAGTGCCCAAAACTG S GTTTCTAAATACCATTCTCCTACAGAAGGGACCAGAGAGAAGTGAATGATTCTAAGATT GGGCAAGGAAGACACAAGATAAACCTAAAACCTTTTGTAGT Celera SNP ID: hCV26478847 Public SNP ID: rs6864396 SNP Chromosome Position: 132075774 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 29552 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; Celera; HapMap Population(Allele, Count): Caucasian (G,34|C,86) SNP Type: INTRON Context (SEQ ID NO: 46): TCTTCCTAGTCCAACTTTAAGTCTCTAAGCTGTCTGTTCCTTCTAGTTTCTGATTAGCCAG AGTTCAGCCAAACGAAAATATGTTTTGATGTTTTGTATG S TCACTGCACACCTGGGTACTTACTAGGCAACGTGAATAAGAGGACACACATTTGCTCCC TCAGTTTATACCAATCACGAGTGCAAGCAAAACAGCTGAGA Celera SNP ID: hCV26478954 Public SNP ID: rs4426908 SNP Chromosome Position: 132050617 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 4395 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; Celera; HGBASE Population(Allele, Count): Caucasian (C,32|G,88) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTERGENIC; UNKNOWN Context (SEQ ID NO: 47): AATAGATAAAATTAAAAATTGCACATAAAGAAATATTTTATTTCTTTTAAATTATAAGC TCACATTATGATTATTTATAAATTCTTAATCAAAGAACTCC Y TTTTAAAAAATCCAGAACTGCATTAGTAGGTAATTTCTAAGAGAACCAACCTCTGACTT TGCAGCCATCAGCATAGTCCAAACTTTCTTTAACTTCTTGG Celera SNP ID: hCV27472356 Public SNP ID: rs3213639 SNP Chromosome Position: 132066098 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 19876 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (C,34|T,86) SNP Type: INTRON Context (SEQ ID NO: 48): TGGTGATGAAACTGAATCAAAGACAATTCATTTCTGGTCATACAGTTTCTTAGAAACAT ACATTCCTTCAGAAAACCAATAACACTTTTCACAGAATTGG W CTGAGGCTCTCCTAGAACCTTTCCGTCATTCTTAACTTCCTTCCCAAAGAACTACAGAAG AAGAGTGTAAATCATAAGAGTGTATAAATTGTTAATGTCT Celera SNP ID: hCV30585769 Public SNP ID: rs10062446 SNP Chromosome Position: 132068273 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 22051 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (T,34|A,86) SNP Type: INTRON Context(SEQ ID NO: 49): TCTAAAAAGGGATGGGGGAGGGGAGCAGGTGGAGAGGAATATCTGATCTGTCATATTC TTTAGTCAGAGCCATAAACGTGTTTGGGTGAGACAATCTGAT S TTCTGATAACGTTGATAAAAGTCACTTGAAGATTGGGTCACAGGAAGCCCAAGATAAAT GCTGAGAGGAAACAGGTTCTTTTTGAAGTAGCCTCTTCGTA Celera SNP ID: hCV31237737 Public SNP ID: rs11242122 SNP Chromosome Position: 132052607 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 6385 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; HapMap; ABI_Val Population(Allele, Count): Caucasian (C,31|G,89) SNP Type: INTERGENIC; UNKNOWN Context (SEQ ID NO: 50): AAACAAGTGATCCACCCACCTCAGCCTCCCAAAGTGCTGGGATTACAGGTGTGAGCCAC CGCGACCAGACTTTTCACCTCTAATTTCTAATTGCTATTAG Y TGCATCACCAGTACTAGGTTTGAGGCATCTGTATAATATCGCTGAAGCTTCCTACTCTTT AGACTAAAAATGTGACACAATTACTAAAAAGAATACAATT Celera SNP ID: hCV31237618 Public SNP ID: rs11242127 SNP Chromosome Position: 132083403 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 37181 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP Population(Allele, Count): Caucasian (C,35|T,85) SNP Type: INTRON Context (SEQ ID NO: 51): TACACTTAAAACAGGGCACATGCTGGGCACAGTGGCTCACGCCTGTAACCTCAGCACTT TGGGAGCCCAAGGCGGGAGGACTGCTTGAAGCCAGGAGTTC Y AAACCAACCTGGGCAACTTAGCAAGCTCCTATCTCTACAAAAAATTAAAAAATTAGCCA AGTGTAGTGGCAGACACCTGTGGTCCCAGCTACTTGGGAGG Celera SNP ID: hCV31237741 Public SNP ID: rs11747814 SNP Chromosome Position: 132052065 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 5843 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (C,31|T,89) SNP Type: INTERGENIC; UNKNOWN Context (SEQ ID NO: 52): TGTGGGGAAACAGAGGGCTGGAGTAGTTGGTCAGGACACAGTGTAGTAAGAAAAGATA GCCTTGGATGTGATGGCATATATGAAAACGGTTCCTCAACTA W TACTACTCTATTCAGTTAACCTCAACTTTTCTCAGACTTTTTTTTGTGAAAATAATCATAA CACACTTTTCAGACTTTTCCGTGAAAATAAATGAATCAT Celera SNP ID: hCV31237596 Public SNP ID: rs3798129 SNP Chromosome Position: 132091459 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 45237 Related Interrogated SNP: hCV16176374 (Power = .51) SNP Source: dbSNP; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (T,99|A,21) SNP Type: INTRON Context (SEQ ID NO: 53): TTCTATTAAGATTTGGTCTTTTTCTATTAATTGGTTCATAAGAATGTAATTGATTTTTGAA GTTTTGTTGTTTGATTTCCTTGGGTTTGATATATCTATC R TCAAGTCACAGAAACTTATAAAAATTTTTCATTTCCTATTTCTCATCTTTACCTCTAATC CCTCTTTTTTTCTTTTTTTTTTGGTTGCTGTGTTGCCCA Celera SNP ID: hCV31237619 Public SNP ID: rs4705965 SNP Chromosome Position: 132083026 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 36804 Related Interrogated SNP: hCV16176374 (Power = .6) SNP Source: dbSNP; HGBASE Population(Allele, Count): Caucasian (G,85|A,35) SNP Type: INTRON Context (SEQ ID NO: 54): ATTTATATGACATTTCAAGAAAAGGCAAAATTATAGTGACAGAAAGCAGATCAATAGTT GCTTGGGGGTGGGTCTGCAAATGGGCATAAAGGAAATTTTG R GGATGACAAGGGTGTTCTAAAGCTGGATTGTGATGATGGTTTCACAACTGTATATATTT ACTACACTCATCAAACTGCACAGCTGCAATATGTGAATTTA Celera SNP ID: hCV30117349 Public SNP ID: rs9784675 SNP Chromosome Position: 132097638 SNP in Genomic Sequence: SEQ ID NO: 11 SNP Position Genomic: 51416 Related Interrogated SNP: hCV16176374 (Power = .51) SNP Source: dbSNP; HapMap; ABI_Val Population(Allele, Count): Caucasian (A,99|G,21) SNP Type: INTRON Gene Number: 3 Gene Symbol: IL4-3565 Gene Name: interleukin 4 Chromosome: 5 OMIM NUMBER: 147780 OMIM Information: Genomic Sequence (SEQ ID NO: 12): SNP Information Context (SEQ ID NO: 55): TATTCTAAATACTTGAAAACTTTAAATGTATTCATTCCTCAGAGCAACTTCATGAGACAG GGACAGCTATGACCCCTATTTCACAGATGAGGCTGAGTAG M GTGCCCAAGGTCACACAGCCAGGAGGCACAGCAGCCAGGCCTGACAGACCACCTGGGC CCAGCGTCCGCTCTCTTAGCCACCGTGTACTATAGCAGCCTC Celera SNP ID: hCV2259917 Public SNP ID: rs762534 SNP Chromosome Position: 132032655 SNP in Genomic Sequence: SEQ ID NO: 12 SNP Position Genomic: 5383 SNP Source: dbSNP; Celera; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (C,111|A,9) SNP Type: INTERGENIC; UNKNOWN Context (SEQ ID NO: 56): CACAGCAGGGAGAGTGCTGTGTTATGCGAGGAGGTTGGAGAAATCCTCCCCATGAGAT AAGATGGGAACAGAGATCGGGACGAAACAAGGGGAGGGGACA M CCATGCACAGATCGGGGCAAAGTCATCAAGGGAAAGGGAACTGCAGGTGCTAAGGTCT TGAGCAAGAGCGAGCTGGGGATGCCCTTCCCAGCACTCACGG Celera SNP ID: hCV16176063 SNP Public SNP ID: rs2243211 SNP Chromosome Position: 132029321 SNP in Genomic Sequence: SEQ ID NO: 12 SNP Position Genomic: 2049 SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (C,108|A,12) SNP Type: TFBS SYNONYMOUS; INTERGENIC; UNKNOWN Context (SEQ ID NO: 57): GGCAGACACACTCAGCAGCCAGAGCTAGACAGGCAGGTGGTAGGAGTCCAGGGCCACG GCAGGGATGGAGTGTCGCCCCCTCGCTGCGATACCAGAGCAA S TAAAACGTTAAGGCCTTGCACTAAAGCTGCCCTTAGGATGCATTCTTTTAAAGTTTTTCC ATTTAATGCAGACTCTTTTCAATTCTTATTTTATCCTTGT Celera SNP ID: hCV16176374 Public SNP ID: rs2227282 SNP Chromosome Position: 132041078 SNP in Genomic Sequence: SEQ ID NO: 12 SNP Position Genomic: 13806 SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (G,32|C,86) SNP Type: INTRON Context (SEQ ID NO:58): GGGTGTCTATGAAGTCAAGGCTGTCTGAGGAACAGCAAAGTGGGAAGAAGCAAGCTGG CTGGCTGATGAAGGGTTTCTTGGGTGGACAAGTAGTTGGAGC K ATTTCCTATTTACCAAAGAGAGCTAAAGTTCATAATTCTACAGAGAGTTCCATAATGAA CCTCAAATACCTCTGTTTTTTGAAGGAGTTTCTCATATACA Celera SNP ID: hCV11818513 Public SNP ID: rs2227284 SNP Chromosome Position: 132040624 SNP in Genomic Sequence: SEQ ID NO: 12 SNP Position Genomic: 13352 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (T,32|G,86) SNP Type: INTRON Context (SEQ ID NO: 59): GGCCAGGATGGTCTCAATCTCTTGACCTCGTGATCCACCTGCCTCAACCTCCCAAAGTG CTGGGATTACAGGGGTGAGCCACCACACCCGGCCCAGCAGT W GTTTTTCTAAAACACAAATAGGCCAGGTGCAGTGGCTCACGCCTGTAATCCCGGCACTT TGGGAGACTGAGATGGGAGCTTCGCTTGAACCCAAAAGTTC Celera SNP ID: hCV15793498 Public SNP ID: rs2406539 SNP Chromosome Position: 132051175 SNP in Genomic Sequence: SEQ ID NO: 12 SNP Position Genomic: 23903 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (A,29|T,77) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTERGENIC;UNKNOWN Context (SEQ ID NO: 60): TCTTCCTAGTCCAACTTTAAGTCTCTAAGCTGTCTGTTCCTTCTAGTTTCTGATTAGCCAG AGTTCAGCCAAACGAAAATATGTTTTGATGTTTTGTATG S TCACTGCACACCTGGGTACTTACTAGGCAACGTGAATAAGAGGACACACATTTGCTCCC TCAGTTTATACCAATCACGAGTGCAAGCAAAACAGCTGAGA Celera SNP ID: hCV26478954 Public SNP ID: rs4426908 SNP Chromosome Position: 132050617 SNP in Genomic Sequence: SEQ ID NO: 12 SNP Position Genomic: 23345 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; Celera; HGBASE Population(Allele, Count): Caucasian (C,32|G,88) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTERGENIC; UNKNOWN Context (SEQ ID NO: 61): TCTAAAAAGGGATGGGGGAGGGGAGCAGGTGGAGAGGAATATCTGATCTGTCATATTC TTTAGTCAGAGCCATAAACGTGTTTGGGTGAGACAATCTGAT S TTCTGATAACGTTGATAAAAGTCACTTGAAGATTGGGTCACAGGAAGCCCAAGATAAAT GCTGAGAGGAAACAGGTTCTTTTTGAAGTAGCCTCTTCGTA Celera SNP ID: hCV31237737 Public SNP ID: rs11242122 SNP Chromosome Position: 132052607 SNP in Genomic Sequence: SEQ ID NO: 12 SNP Position Genomic: 25335 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; HapMap; ABI_Val Population(Allele, Count): Caucasian (C,31|G,89) SNP Type: INTERGENIC; UNKNOWN Context (SEQ ID NO: 62): TACACTTAAAACAGGGCACATGCTGGGCACAGTGGCTCACGCCTGTAACCTCAGCACTT TGGGAGCCCAAGGCGGGAGGACTGCTTGAAGCCAGGAGTTC Y AAACCAACCTGGGCAACTTAGCAAGCTCCTATCTCTACAAAAAATTAAAAAATTAGCCA AGTGTAGTGGCAGACACCTGTGGTCCCAGCTACTTGGGAGG Celera SNP ID: hCV31237741 Public SNP ID: rs11747814 SNP Chromosome Position: 132052065 SNP in Genomic Sequence: SEQ ID NO: 12 SNP Position Genomic: 24793 Related Interrogated SNP: hCV16176374 (Power = .7) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (C,31|T,89) SNP Type: INTERGENIC; UNKNOWN Gene Number: 4 Gene Symbol: IL5-3567 Gene Name: interleukin 5 (colony-stimulating factor, eosinophil) Chromosome: 5 OMIM NUMBER: 147850 OMIM Information: Genomic Sequence (SEQ ID NO: 13): SNP Information Context (SEQ ID NO: 63): TCTGCCACAGGCAGATCCGGGTGATGGTCAGCTTGGCTGTGTGAATGGGACTCAGTGGA AAGGACTGCCTTCTGTGGGGTGCAGAACAGGGGCATTACTT K TTGAATGTCCTGATGAGCCACACACAGATCTAACCATGCTGTATCAGGAGAGAGTCAGT GCTAGTCATGCACAGTCATGCACAAGGGAACTTGCAAACAC Celera SNP ID: hCV28028637 Public SNP ID: rs4143832 SNP Chromosome Position: 131890876 SNP in Genomic Sequence: SEQ ID NO: 13 SNP Position Genomic: 20000 SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (T,17|G,103) SNP Type: INTRON Context (SEQ ID NO: 64): TTCAGCTACAGAGACGGAGGCAATGTGAACTACCATTTAGATTTTTTAAAATCCACG TTATTTTAGGGAAATAGAAAGAAATAAACAATTAGTACTTT S ATTTTTAAATAATTACTGGTTGGTAATTTTATAATTTAAAAAAAGCACTTTGATTAATAT TGAAAAGTTACATTGGTGGGTAACATCCATCCTTTTGCCC Celera SNP ID: hCV11818554 Public SNP ID: rs12652920 SNP Chromosome Position: 131913139 SNP in Genomic Sequence: SEQ ID NO: 13 SNP Position Genomic: 42263 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; ABI_Val Population(Allele, Count): Caucasian (G,93|C,25) SNP Type: TFBS SYNONYMOUS;INTRON Gene Number: 5 Gene Symbol: LOC441108-441108 Gene Name: hypothetical gene supported by AK128882 Chromosome: 5 OMIM NUMBER: OMIM Information: Genomic Sequence (SEQ ID NO: 14): SNP Information Context (SEQ ID NO: 65): TCTGCCACAGGCAGATCCGGGTGATGGTCAGCTTGGCTGTGTGAATGGGACTCAGTGGA AAGGACTGCCTTCTGTGGGGTGCAGAACAGGGGCATTACTT K TTGAATGTCCTGATGAGCCACACACAGATCTAACCATGCTGTATCAGGAGAGAGTCAGT GCTAGTCATGCACAGTCATGCACAAGGGAACTTGCAAACAC Celera SNP ID: hCV28028637 Public SNP ID: rs4143832 SNP Chromosome Position: 131890876 SNP in Genomic Sequence: SEQ ID NO: 14 SNP Position Genomic: 126304 SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (T,17|G,103) SNP Type: INTRON Context (SEQ ID NO: 66): ATCTGACTTCCCCCTAGCCGACTTAACCCTCAGGTGGGCCCCCTTCTCATCATTATCAGG CTCTGAATCCCCACACTATTCCCTGGCGTGGACACTCTGC K TCTGAGCTGGATCCCCCCTATTTGTGGATTCCTCTTTCATCCTGCTGGAACTCTGATACC ATTGGACTCTTGAAACTCTGTCTATGCCAAGCTGTTTCCC Celera SNP ID: hCV2549985 Public SNP ID: rs2299015 SNP Chromosome Position: 131929396 SNP in Genomic Sequence: SEQ ID NO: 14 SNP Position Genomic: 164824 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (T,89|G,21) SNP Type: INTRON Context (SEQ ID NO: 67): CATTAACAGAATAAAAAAGAAATATCATATGAACACCAATAGATACAGAAAACGCATT TCAATTTAATAGTCTTTCGTGGTAAAAACCCTCAGCATAACT R ATGGAGGAATATTAATTCCTGAATAATTCAGTCCAAAATCCGTTAGGTGTGGCTGAGTG AGGAAGGCATCCACAAAAACAAGCTGTGACAGCCTGGTGGG Celera SNP ID: hCV11818538 Public SNP ID: rs2706348 SNP Chromosome Position: 131933709 SNP in Genomic Sequence: SEQ ID NO: 14 SNP Position Genomic: 169137 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (G,94|A,26) SNP Type: INTRON Context (SEQ ID NO: 68): AGTTCAGCTACAGAGACGGAGGCAATGTGAACTACCATTTAGATTTTTTAAAATCCACG TTATTTTAGGGAAATAGAAAGAAATAAACAATTAGTACTTT S ATTTTTAAATAATTACTGGTTGGTAATTTTATAATTTAAAAAAAGCACTTTGATTAATAT TGAAAAGTTACATTGGTGGGTAACATCCATCCTTTTGCCC Celera SNP ID: hCV11818554 Public SNP ID: rs12652920 SNP Chromosome Position: 131913139 SNP in Genomic Sequence: SEQ ID NO: 14 SNP Position Genomic: 148567 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; ABI_Val Population(Allele, Count): Caucasian (G,93|C,25) SNP Type: TFBS SYNONYMOUS; INTRON Context (SEQ ID NO: 69): AAAATATACTGTGCCAGCTAATGAAAAGAAAGCTGAAGTGACCATATTAATACCAGAC AGAAGACATCAGAACTAGGGAAAATGAAACATTTTATGATGA K AAGAGGTCAATTTAATGCAAGGACACAAAAATTCTAAATGTAAAACTTCCAAATATATG AGGCAGAAACCTATGGAACAGAATGGAGAAATACACAAATC Celera SNP ID: hCV16274080 Public SNP ID: rs2706347 SNP Chromosome Position: 131933016 SNP in Genomic Sequence: SEQ ID NO: 14 SNP Position Genomic: 168444 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HGBASE Population(Allele, Count): Caucasian (G,92|T,26) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 70): GTATGCCAGTCTTCCTTATCCTGCGTAGGCTCTTGACTCTTCATTCTGACTCCCTCATGG GATGCTTTTCCATTCTGTATACACTTTGACTTCTGGTGCT R GATCATGTCCTCATTCCCCGCCCTTCTCTGATACCAGGTGGCCTCTTGCAGGGATGCCCA CTGTACTCTGACCTGAATCTCCCCAGATCTGTGTCACTGA Celera SNP ID: hCV16274083 Public SNP ID: rs2244012 SNP Chromosome Position: 131929124 SNP in Genomic Sequence: SEQ ID NO: 14 SNP Position Genomic: 164552 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (A,94|G,26) SNP Type: INTRON Context (SEQ ID NO: 71): CACACACACACACACACACACATATATATATAGTTTATTCGTTTTTAGTGGGGTCATGTG CCTCTCTAGCCGCTCCCTGACCCCAGTTCCACTATGTTTT Y GTTACGTATTCTTCCAAGCTTTTTTCTGCTTATAAAACCATGTCTACGTGAGCACATAAA TGCATTCACACATACATATTTTAGCACATTAATATAATTT Celera SNP ID: hCV16274086 Public SNP ID: rs2706338 SNP Chromosome Position: 131923748 SNP in Genomic Sequence: SEQ ID NO: 14 SNP Position Genomic: 159176 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (C,94|T,26) SNP Type: INTRON Gene Number: 6 Gene Symbol: P4HA2-8974 Gene Name: procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase), alpha polypeptide II Chromosome: 5 OMIM NUMBER: 600608 OMIIM Information: Genomic Sequence (SEQ ID NO: 15): SNP Information Context (SEQ ID NO: 72): TCCTACACACAGCTACTTCTCAGGGACTGCTGGTCCTAGAGCCTTAGGTGGGGCCTTTTC CAACACTGTGTGCACTTCAAGCCCAGATCCGCCATTTTCT S TTCATCTTCCCTGGGATGAGACAGCTTCTTGTTTAACTCTCCACACATAGAAGTTCTCAT TATACATTCTCTTAGAGCTCCTGGTTTCTCCTGCAGGAGA Celera SNP ID: hCV559494 Public SNP ID: rs334902 SNP Chromosome Position: 131614458 SNP in Genomic Sequence: SEQ ID NO: 15 SNP Position Genomic: 68256 SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (G,44|C,70) SNP Type: MICRORNA; UTR3, INTRON Context (SEQ ID NO: 73): GAGCACTCACAGCCGGGCTCCTCCCCTGGCACATGGCCAGCTGCCAATATGAAGCAGC AATCTCAGAGTACGAGCTACCCACAGGCAAGAATAGTGCCTC S GCCACCTCCTCTCTCTCGTCCCTCATTCTAGCCCCAGGCACGCTCCAGTCAGAAGCCAA ACAGGCAGAAGGCACTCAAGGGATGAACTGAGATCAGCTGG Celera SNP ID: hCV2346958 Public SNP ID: rs157578 SNP Chromosome Position: 131589221 SNP in Genomic Sequence: SEQ ID NO: 15 SNP Position Genomic: 43019 SNP Source: dbSNP; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (G,110|C,8) SNP Type: INTRON Context (SEQ ID NO: 74): AGCCTTAGGTGGGGCCTTTTCCAACACTGTGTGCACTTCAAGCCCAGATCCGCCATTTTC TCTTCATCTTCCCTGGGATGAGACAGCTTCTTGTTTAACT M TCCACACATAGAAGTTCTCATTATACATTCTCTTAGAGCTCCTGGTTTCTCCTGCAGGAG AGGCACAGACTCCGTTGTTCCTAAGCAAGCCTATCTTCCC Celera SNP ID: hCV26479272 Public SNP ID: rs4594848 SNP Chromosome. Position: 131614497 SNP in Genomic Sequence: SEQ ID NO: 15 SNP Position Genomic: 68295 SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (C,62|A,58) SNP Type: MICRORNA; UTR3; INTRON Context (SEQ ID NO: 75): CCTAGTACCTCAAGCCAAAGCTGTTCCAAGATGAGGTCCAGGGGCCAGGATAGGAACC TATTTCAGGTCTCTCCATGAGTGTCGGGCCTCCTGGTCACCT Y GGCCCCTACAATTCTATTAGGAGCAGAGCAATAGAGAATCGAGCTAAATCCTCCCTCCC AATCATCAGTGATGGGATATGGCAGCAAATAGGTCTCTTTG Celera SNP ID: hDV70977429 Public SNP ID: rs17618604 SNP Chromosome Position: 131570083 SNP in Genomic Sequence: SEQ ID NO: 15 SNP Position Genomic: 23881 SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (C,104|T,14) SNP Type: INTRON Gene Number: 7 Gene Symbol: RAD50-10111 Gene Name: RAD50 homolog (S. cerevisiae) Chromosome: 5 OMIM NUMBER: 604040 OMIM Information: Genomic Sequence (SEQ ID NO: 16): SNP Information Context (SEQ ID NO: 76): TTACAATTAGATTCCTGGGAAGCACAAACATTTAAAATGTTAGCAGGCAAAGGAAAAG GAGCAAGTGAAGGAAACTGGGATGTGCCCAGGGAACCAGGGT K AGAGCCAGATAGCTGTGGTGTCTAGGTGCCTTGGGAAAATAGTCTTCAAGGAGAAGGT CAACAGTGTTTGGTGCTACGTAGAGGTAGAGTAAGATGAAGA Celera SNP ID: hCV16154343 Public SNP ID: rs2897443 SNP Chromosome Position: 131957493 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 46964 SNP Source: dbSNP; Celera; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (G,94|T,26) SNP Type: INTRON Context (SEQ ID NO: 77): ACCACAGTAATAATTATTGCAAGCAAGATCTGTCAGTGGATACTAAAATCAGTAGGCAG AAAGTTGAGAAGCAAGATATTTACATAGTCTCAACATTTCT Y TCCTAAGATACTTGTTAATTACAAAGACTAAAACAGTAACTTTACAGTGGAGAAACCTG GCCAACACCATCTAAGCCAAGTGAATAAGGATTAACATCAT Celera SNP ID: hCV29134417 Public SNP ID: rs6884762 SNP Chromosome Position: 131966629 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 56100 SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (C,112|T,6) SNP Type: INTRON Context (SEQ ID NO: 78): TTTTAAAAGAATTTTAGCCTCAGCCTGGTATCCTTTGAGAGTTACTAGAAGGTGAACAG TGTTTTGATACAATTATATTTCATGGCCTTAAGAACTTTAT Y ACTGAAGAAGTGATATAGAATTGGTAATCACACTGTAGTGATGGGCCTTAGTTATTCAG TATTGAACATGCTTAGCCCCCATACATCTGCTAGCCTGCTG Celera SNP ID: hCV15756644 Public SNP ID: rs2301713 SNP Chromosome Position: 131979895 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 69366 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_Val; HGBASE Population(Allele, Count): Caucasian (T,91|C,25) SNP Type: INTRON Context (SEQ ID NO: 79): GGCTGAGGAACTGGGGCATCTGGGTTGCTTCTGGCCAGACCACCAGGCTCTTGAATCCT CCCAGCCAGAGAAAGAGTTTCCACACCAGCCATTGTTTTCC Y CTGGTAATGTCAGCCTCATCTGTTGTTCCTAGGCTTACTTGATATGTTTGTAAATGACAA AAGGCTACAGAGCATAGGTTCCTCTAAAATATTCTTCTTC Celera SNP ID: hCV16164126 Public SNP ID: rs2074369 SNP Chromosome Position: 132001562 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 91033 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (T,91|C,25) SNP Type: INTRON Context (SEQ ID NO: 80): AAGAGCGTGAACTCTGGAGCTGGACTGGGTTAAAATCTTGGCTGCTTCATTTACTGGCT GTTGTGACCCTGGACAAATCATTCTCTGTGTCTGTTTGCTC R TTTGAAATATAGGACTGAAAATGATGGTACTGTCCTCATAGGGTCATTGAAAGGAGCAA ATGAGTTGACTTTTAGAGCCTGGCACATAGAAAGTGCTTTT Celera SNP ID: hCV25471631 Public SNP ID: rs2522394 SNP Chromosome Position: 131972028 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 61499 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: Applera Population(Allele, Count): Caucasian (A,6|G,34) African American (A,26|G,12) total (A,32|G,46) SNP Type: INTRON SNP Source: dbSNP; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (G,94|A,26) SNP Type: INTRON Context (SEQ ID NO: 81): GTTCAAGACTCTTCTGAGCAGTGATACCAGCAATTTAGTTCAGTCCTAAAGAACTGAGG GTCCTGGGAATTTAACCATGTCAGTGTAGTCTTTTTTACAT Y GCTAGCATAAAAATGGGTGCCCTTTAAAGATTTTTTTAAGATTAAGAAACTAGAAGACA GGAGGAGCCAAATCAGGACTGTAAGGTGGATGCCTAATGAT Celera SNP ID: hCV2549970 Public SNP ID: rs10463893 SNP Chromosome Position: 131955938 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 45409 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap Population(Allele, Count): Caucasian (T,93|C,25) SNP Type: INTRON Context (SEQ ID NO: 82): AATGGAAGTAAGTCGCCTCACATCCTCACATCCTTTGTATGATGATCGCAGTAATGTTTG CCTTTTTACCACTGGTGCAACATTTATTAGTTCATAAACC Y TTCCAAAGAACCTAAGGGCTTTTTGATGAAAGGTGACTGTGGTGGGAACAATGTTAG GTTTTGACAGGCTACTTTTGCTTTTACAAACATTTCTCCTC Celera SNP ID: hCV2549979 Public SNP ID: rs12653750 SNP Chromosome Position: 131999801 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 89272 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap Population(Allele, Count): Caucasian (C,94|T,26) SNP Type: INTRON Context (SEQ ID NO: 83): GTAAAATAAAATTTTGATGTCAGTTCAGCTTAAGCTTCTAAATCATTCTCGCTGAACCAA AGCTTCCAGTTGGTAAAAATCTAAGATCTGAGGAATAAAA S CAACATAAGAAGCTGACTTTTGGTAAGCCTTAACAGTGATAGAAGAGGGACAAGGTGG CAGTTCGATTCTCCTATTTTCCCCATAGACTGCAGGCCCCTT Celera SNP ID: hCV2549980 Public SNP ID: rs2040703 SNP Chromosome Position: 132000157 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 89628 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (C,92|G,26) SNP Type: INTRON Context (SEQ ID NO: 84): CAAGGTTGTCAAGCAGCTCTTGCATAAGGACTATGCTGGCAGAGAAAAAGGCAGTTTCC CTGAGGTCAAAGGAGGTGTTTCAGGCACTCCTGGCTCCAGA Y TCCCTTTCTGGCAGAGAGGGCCAGAAGGAGAGCTCAGCAGCGCAGGGCCACCTTTC TGCAGCCATCATCACAAGTAAGGGCGAGTGCTTTTGAAACCT Celera SNP ID: hCV2549981 Public SNP ID: rs2240032 SNP Chromosome Position: 132005026 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 94497 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; ABI_VaP HGBASE Population(Allele, Count): Caucasian (C,92|T,24) SNP Type: INTRON Context (SEQ ID NO: 85): ATCTGACTTCCCCCTAGCCGACTTAACCCTCAGGTGGGCCCCCTTCTCATCATTATCAGG CTCTGAATCCCCACACTATTCCCTGGCGTGGACACTCTGC K TCTGAGCTGGATCCCCCCTATTTGTGGATTCCTCTTTCATCCTGCTGGAACTCTGATACC ATTGGACTCTTGAAACTCTGTCTATGCCAAGCTGTTTCCC Celera SNP ID: hCV2549985 Public SNP ID: rs2299015 SNP Chromosome Position: 131929396 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 18867 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (T,89|G,21) SNP Type: INTRON Context (SEQ ID NO: 86): GAGCCTAACAGGACTTACATATTTGACTGCAGTAGGCATTATATTTAGCTGATGACATA ATAGGTTCTGTCATAGTGTAGATAGGGATAAGCCAAAATGC R AAGAAAAACCATCCAGAGGAAACTCTTTTTTTTTTCTTTTTCTTTTTTTTTTTTCCAGA TGGAGTCTCGCACTTCTCTGTCACCCGGGCTGGAGCGCA Celera SNP ID: hCV 11740472 Public SNP ID: rs2040704 SNP Chromosome Position: 132001076 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 90547 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (A,94|G,26) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 87): CATTAACAGAATAAAAAAGAAATATCATATGAACACCAATAGATACAGAAAACGCATT TCAATTTAATAGTCTTTCGTGGTAAAAACCCTCAGCATAACT R ATGGAGGAATATTAATTCCTGAATAATTCAGTCCAAAATCCGTTAGGTGTGGCTGAGTG AGGAAGGCATCCACAAAAACAAGCTGTGACAGCCTGGTGGG Celera SNP ID: hCV11818538 Public SNP ID: rs2706348 SNP Chromosome Position: 131933709 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 23180 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; ABI_Val; HGBASE Population(Allele, Count): Caucasian (G,94|A,26) SNP Type: INTRON Context (SEQ ID NO: 88): AGTTCAGCTACAGAGACGGAGGCAATGTGAACTACCATTTAGATTTTTTAAAATCCACG TTATTTTAGGGAAATAGAAAGAAATAAACAATTAGTACTTT S TTTTAAATAATTACTGGTTGGTAATTTTATAATTTAAAAAAAGCACTTTGATTAATAT TGAAAAGTTACATTGGTGGGTAACATCCATCCTTTTGCCC Celera SNP ID: hCV11818554 Public SNP ID: rs12652920 SNP Chromosome Position: 131913139 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 2610 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; ABI_Val Population(Allele, Count): Caucasian (G,93|C,25) SNP Type: TFBS SYNONYMOUS; INTRON Context (SEQ ID NO: 89): AAATGGGAATTGCTAGAGGAATATGTTTCTAACTTTTTTTTTCTGCACACAAGTTCATGG TCCTTCTATACTTGATAGCCCAGCTGCTCTATTTTTTTTA K CTCATTTCACACTGGCACCTAAGCTTTAAACCCTTGGCTGTTTTTGTTATTGTTGTCTGCC TGCTTGTCCTAGCTCCTTTTACTAGGTGTGTACTCTGGG Celera SNP ID: hCV15793510 Public SNP ID: rs2252775 SNP Chromosome Position: 131946343 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 35814 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (T,94|G,26) SNP Type: INTRON Context (SEQ ID NO: 90): AACTTTTTTTAACTGCATATATACAATACATATGTATATACAAACATTCACTTGTATGTA TGTGCATATTTACCTTTACAAGCTTAATCAGAACAGGATT R AGAGGAAGAGTCTGGGTTAAGTAAATCAATATAAAAGGAGTAAATGACCAATATGA ATCGGGGTCAGATTATAGAAAGCTAATGGATTTCAGACTTAG Celera SNP ID: hCV15892310 Public SNP ID: rs2246176 SNP Chromosome Position: 131945249 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 34720 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (A,93|G,25) SNP Type: INTRON Context (SEQ ID NO: 91): TTGTAGTCCATTAAGTTATTGAACTGTGTTTGCAATTTGGATGCTTCTTTTTAACAGAAA AAATTTAAAAAGCAGAAAGGTCATCAGTTACTACCTCTCA Y CCAGCAGCAACCATTGGGCATATTTTCTTTCACTTGTCTTTCTGTGCGTATATTTATTTAC CTGATTGACAGTATAAATGTAATTTTTTTCACTTACCAT Celera SNP ID: hCV15892315 Public SNP ID: rs2522403 SNP Chromosome Position: 131943216 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 32687 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (T,94|C,26) SNP Type: INTRON Context (SEQ ID NO: 92): TTGGTACATAACAGTACTTTTTACATTCATTCACATTACTAAGTACAACTATTTTTCAAA CGTTTCTTCAACCAGGACATAACAGAAATGCCAACAAGAC W TAATCCAGTCGACTCTGTAAGGATGCCCATAAGCTTGAGAGTAGTGCCTGACACATA AGAGGATGCTTAATAAGTGATAATAAATAAATGAAGATTTT Celera SNP ID: hCV16098376 Public SNP ID: rs2106984 SNP Chromosome Position: 131980965 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 70436 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; Celera; HapMap; HGBASE Population(Allele, Count): Caucasian (T,94|A,26) SNP Type: INTRON Context (SEQ ID NO: 93): AAAATATACTGTGCCAGCTAATGAAAAGAAAGCTGAAGTGACCATATTAATACCAGAC AGAAGACATCAGAACTAGGGAAAATGAAACATTTTATGATGA K AAGAGGTCAATTTAATGCAAGGACACAAAAATTCTAAATGTAAAACTTCCAAATATATG AGGCAGAAACCTATGGAACAGAATGGAGAAATACACAAATC Celera SNP ID: hCV16274080 Public SNP ID: rs2706347 SNP Chromosome Position: 131933016 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 22487 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HGBASE Population(Allele, Count): Caucasian (G,92|T,26) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Context (SEQ ID NO: 94): GTATGCCAGTCTTCCTTATCCTGCGTAGGCTCTTGACTCTTCATTCTGACTCCCTCATGG GATGCTTTTCCATTCTGTATACACTTTGACTTCTGGTGCT R TCATGTCCTCATTCCCCGCCCTTCTCTGATACCAGGTGGCCTCTTGCAGGGATGCCCA CTGTACTCTGACCTGAATCTCCCCAGATCTGTGTCACTGA Celera SNP ID: hCV16274083 Public SNP ID: rs2244012 SNP Chromosome Position: 131929124 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 18595 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_Val HGBASE Population(Allele, Count): Caucasian (A,94|G,26) SNP Type: INTRON Context (SEQ ID NO: 95): CACACACACACACACACACACATATATATATAGTTTATTCGTTTTTAGTGGGGTCATGTG CCTCTCTAGCCGCTCCCTGACCCCAGTTCCACTATGTTTT Y GTTACGTATTCTTCCAAGCTTTTTTCTGCTTATAAAACCATGTCTACGTGAGCACATAAA TGCATTCACACATACATATTTTAGCACATTAATATAATTT Celera SNP ID: hCV16274086 Public SNP ID: rs2706338 SNP Chromosome Position: 131923748 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 13219 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_VaP HGBASE Population(Allele, Count): Caucasian (C,94|T,26) SNP Type: INTRON Context (SEQ ID NO: 96): AGTGCTAGGATTACAGGCATAAGCCACCATGCCCAACCAAGAGGACTTTTTTTTTTTTTA AATATGGGAAAGCAATTGCCTAAGAGTGCAGTCAGGTTGA Y CAAATTCCAACTCTGCTGGGTGACCTTAGGGATGTTACTTAACCCATTTGGGCCTTCG TATCCCCCACGTAAAGTGAGGGGATTGATTTACATGATCA Celera SNP ID: hCV16274088 Public SNP ID: rs2706372 SNP Chromosome Position: 131963376 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 52847 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (C,94|T,24) SNP Type: INTRON Context (SEQ ID NO: 97): GAGAAAGAAAGGCCTAAGAAGATGTGGTTTGTGGCAGCCAACTTCTAAGATGGTCCCT AGTAATCTCGGCCTCTTGGTATTTATACTCTTGTATAGTCCC M TCTCATGCTGTTGCATGATTGGTCTATATAACTGATAAAACAGGCAGAAGTGATAGCAT GGCCTTTCCAACATTAGGTTATTTAAGGTACTATGGCTTCT Celera SNP ID: hCV16274090 Public SNP ID: rs2706370 SNP Chromosome Position: 131960915 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 50386 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (A,91|C,25) SNP Type: INTRON Context (SEQ ID NO: 98): GAGGTGTTATAGTGCCAGGGAAAAGGGGTGCAAGACTAGTGTTTGCCTCTTATTTCTTG TCTTGTTTATAAATTCTCTACATTAAAAATATAAATTCCAA R GAAAACAATGGAATAAACACAAACTCCCACTTTATAACCAACTAAAATGACAGTAAC TGAATTTTCTAAAGTTTTAAACCCACCAAAATGGGAGAAATG Celera SNP ID: hCV27452177 Public SNP ID: rs3091307 SNP Chromosome Position: 132017035 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 106506 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_VaP HGBASE Population(Allele, Count): Caucasian (A,94|G,26) SNP Type: INTRON Context (SEQ ID NO: 99): CTCCATAGTCTGTTTTTGACCTGGGCCTGGGATAAGAAGGAAGGGCTTTTCAGCAGAGA AAAGTGCCTCAGTCACAGAGGCATGCTGAGCAGAATGAGGA Y CTATGAGGGAGCTTGGTGGAGCCTGGCGGGGTGGAGAGGTACAGGAGTGCTTTCTAGC AATTTAGGCTTGTTAAGGAACAAAGCCAGGCTGTTTACTGAG Celera SNP ID: hCV27484753 Public SNP ID: rs3798135 SNP Chromosome Position: 131993008 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 82479 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; HGBASE Population(Allele, Count): Caucasian (C,94|T,26) SNP Type: INTRON Context (SEQ ID NO: 100): GTCACAGAGGCATGCTGAGCAGAATGAGGACCTATGAGGGAGCTTGGTGGAGCCTGGC GGGGTGGAGAGGTACAGGAGTGCTTTCTAGCAATTTAGGCTT R AAGGAACAAAGCCAGGCTGTTTACTGAGTTCTCTTACCTGGTACTCAGATGGATTCTT CTAAATGAAAAGAGGTGATACAAATTTCCCCAAAATTGAG Celera SNP ID: hCV25812184 Public SNP ID: rs3798134 SNP Chromosome Position: 131993078 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 82549 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap; ABI_VaP HGBASE Population(Allele, Count): Caucasian (G,93|A,25) SNP Type: INTRON. Context (SEQ ID NO: 101): AGAGAGAAACAAACATCAGTGCAGTGGAAGCACCCAAGGCTACACCTGAATGGTGGGA AGCTCTTTGCTGCTATATAAAATGAATCAGGCTCAGCTACTA w TATTACACTCTCCTGAAGCTAACCAACATTTCCTGCAACATTATGTAGACTTTTAAAAGA AGGGCCTGAAGCATTCTCACAGGATAGGCTAAATGTAGAA Celera SNP ID: hCV29134412 Public SNP ID: rs7737470 SNP Chromosome Position: 132001962 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 91433 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (T,94|A,26) SNP Type: INTRON; PSEUDOGENE Context (SEQ ID NO: 102): ATATGATTTAATTCGTATTGTGCCTTTGACATGAGCAAAATGATGATGAAGCCTGTTATC CATAACTTCTTAGTGTTGATAATTGTGCATTCTCAGTTCA Y TCAGAAGATTGTGAATTTGTAATATTAATCACCAGAGTGCTTTACTTAGCGTCTAAA GGCCAGGAACTTTATCAGTGATCACATGCTATGAGGGTTCT Celera SNP ID: hCV29134413 Public SNP ID: rs7449456 SNP Chromosome Position: 131981326 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 70797 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (C,91|T,23) SNP Type: INTRON Context (SEQ ID NO: 103): AGAATATGGAGCTGTGGACCTTGTCTGGGTCAGAGCTTGTACAGTCTATAATATTGTCA ATGGAGTTGTTGCCAGAGTGATTTATCCACAGAGATTCATT Y TAGATTTTCTCATTTGTAATTATGTGTCCACCAGAGCATTTCTTTTGAAAGATGAGCCTC ATTCTTCCTTTATCTGGTCTTATTGTTACTTATTGTAGTT Celera SNP ID: hCV29134415 Public SNP ID: rs6596086 SNP Chromosome Position: 131980121 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 69592 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (T,94|C,26) SNP Type: INTRON Context (SEQ ID NO: 104): CTAATTTTTATGTTTCCATAGGTGACCAGCTGCATGCATTAACTGTCATCTGAGTTGGAT GATTGGGGTTAATATTTAAGAGCTGAGGAGCCTAACAGTC R TATTTACGTACTGATTTAAGCCCTGCTAAATTTAGTCCACAGAATAAAATAATTTGGA AACTTAACTGCCTTTCTCAGTTTTCAGCTGAAGGAATATA Celera SNP ID: hCV30171484 Public SNP ID: rs10520114 SNP Chromosome Position: 131976790 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 66261 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (A,94|G,26) SNP Type: INTRON Context (SEQ ID NO: 105): GGATTTTGTTACTGACATTTTATTGGCTGCACAAAAAAAGCTTGCACTTTAATTTACATT TCCCTCTTTACTAGTGAAGTTAAATGTCTTTTTGTATGTT R TCTATTTGTATTTCTTCTCCAAGAATTACCTATTCATATCTCTTTGCCAGTTTTTCTACTA GGTTGTCTGTTTCTTGATTTGTCACATTTTAATAGATTC Celera SNP ID: hCV31237845 Public SNP ID: rs6596087 SNP Chromosome Position: 131996508 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 85979 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (G,93|A,25) SNP Type: INTRON Context (SEQ ID NO: 106): CTTATGGATTCTGTTAAGCATTTTTTAATTTTATTGGGGTTTCTGTTTGGATTGCATTTAA TTTATGGATTATTTGAGGAGAACGGACATCTTTGTAATA Y CATCTACCATTCTGGACTCCTTAGTATGCATCTCCTGCTCAATCATTTCCTTTTATCCT ATTGTAAAGTTTTACAGCTTTTTCCTCTAGCTCCTATAC Celera SNP ID: hCV30081308 Public SNP ID: rs6871536 SNP Chromosome Position: 131997773 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 87244 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (T,94|C,26) SNP Type: INTRON Context (SEQ ID NO: 107): GAGGTAGTAACTGATGACCTTTCTGCTTTTTAAATTTTTTCTGTTAAAAAGAAGCATCCA AATTGCAAACACAGTTCAATAACTTAATGGACTACAAAGT Y TATTTAAGGGTTACAAACCTTGTTGCTGAAAAAATCTCATCAAACTTTTGCTTCAAAGCC TTTCCTTCACTTAAAGGCCAATTAGAATCTTCTTGATGAC Celera SNP ID: hDV70911845 Public SNP ID: rs17166050 SNP Chromosome Position: 131943112 SNP in Genomic Sequence: SEQ ID NO: 16 SNP Position Genomic: 32583 Related Interrogated SNP: hCV50000065 (Power = .51) SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (C,94|T,26) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON Gene Number: 8 Gene Symbol: SLC22A4-6583 Gene Name: solute carrier family 22 (organic cation transporter), member 4 Chromosome: 5 OMIM NUMBER: 604190 OMIM Information: (Rheumatoid arthritis, susceptibility to}180300 (3) Genomic Sequence (SEQ ID NO: 17): SNP Information Context (SEQ ID NO: 108): ATTAGGACATTTGAAGAGAAAATGCTATTTTGGGAATATATTTATGTTAATAAAAGTAC TCCCACTGAAGCAAAAAGGACAATAAAAATAACTGATATAA R AAAGTATCACTTCTAAAAGCACCATTGTTTATACCGGGTCTCTTTTCCAGATTATTACTT CTTATCCATTGGTCTGGTCATGCTGGGAAAATTTGGGATC Celera SNP ID: hCV25603190 Public SNP ID: rs41542912 SNP Chromosome Position: 131699359 SNP in Genomic Sequence: SEQ ID NO: 17 SNP Position Genomic: 51315 SNP Source: Applera Population(Allele, Count): Caucasian (A,2|G,38) African American (A,0|G,34) total (A,2|G,72) SNP Type: TRANSCRIPTION FACTOR BINDING SITE; INTRON SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (G,114|A,6) SNP Type: TRANSCRIPTION FACTOR BINDING SITE;INTRON Gene Number: 9 Gene Symbol: SLC22A5-6584 Gene Name: solute carrier family 22 (organic cation transporter), member 5 Chromosome: 5 OMIM NUMBER: 603377 OMIM Information: Carnitine deficiency, systemic primary, 212140 (3) Genomic Sequence (SEQ ID NO: 18): SNP Information Context (SEQ ID NO: 109): TTGTTTCTTATACCTAAGGTGGCTTGTCACCTTACAAAGCTAACCCCAAACGTAAAATGT AAAGCACAAATAGATTTGGAGTTAGAAGTATTTCATCTCT W GAGTATTAGCAATTATTCATTAAAAAGAAAAAAAAAGTGTTTAGTCTCTTTCTGCCCTC CAATGGTTAATTATTGCATATCATCTTGGAGTCAGGTCCTT Celera SNP ID: hCV15949533 Public SNP ID: rs35306350 SNP Chromosome Position: 131751188 SNP in Genomic Sequence: SEQ ID NO: 18 SNP Position Genomic: 27845 SNP Source: HGBASE;dbSNP Population(Allele, Count): no_pop (A,-|T,-) SNP Type: INTRON Context (SEQ ID NO: 110): AGGGCTAATATAGTGTTTTTCAAACCTTTCTAAACATTTTGGCCACAGAACTTTCATTAA AGTAGGATAATTTAAGTCTAGTAAATGAAATACACCCTAA R TGGCTAAAAGTATGGCTGTTCTGCCTGCAGCCCCTGCCTTCAATTCCCAATGCCCTGCCT CAAGCCTGTCTGTGCCCCCTTGGAAGGCCCAGGGCCCTGT Celera SNP ID: hDV70978086 Public SNP ID: rs17622208 SNP Chromosome Position: 131744949 SNP in Genomic Sequence: SEQ ID NO: 18 SNP Position Genomic: 21606 SNP Source: dbSNP; HapMap Population(Allele, Count): Caucasian (G,57|A,61) SNP Type: UTR5; INTRON

TABLE 3 Primers Primer 1 (Allele- Primer 2 (Allele- Marker Rs number Alleles specific Primer) specific Primer) Common Primer hCV15949533 rs2073644 C/T TTGGAGTTAGAAGTATT GATTTGGAGTTAGAAGTA GGGGGAAGAGGTGGACATCA rs35306350 TCATCTCC TTTCATCTCT (SEQ ID NO:113) (SEQ ID NO:111) (SEQ ID NO:112) hCV16154343 rs2897443 G/T CCACAGCTATCTGGCT CCACAGCTATCTGGCTCTA AGAGTTATGCAAAGGGAAGG CTC (SEQ ID NO:115) AGTTACA (SEQ ID NO:114) (SEQ ID NO:116) hCV16176063 rs2243211 A/C CCGATCTGTGCATGGT CCGATCTGTGCATGGG CCGCTACCGTGGGAAATAGA (SEQ ID NO:117) (SEQ ID NO:118) AC (SEQ ID NO:119) hCV16176374 rs2227282 C/G GCAAGGCCTTAACGTT GCAAGGCCTTAACGTTTT CTCAGCAGCCAGAGCTAGACA TTAG (SEQ ID NO:120) AC (SEQ ID NO:121) A (SEQ ID NO:122) hCV2259917 rs762534 A/C CACAGATGAGGCTGAG ACAGATGAGGCTGAGTAG GGTCTGTTAACAGAGGCTGCT TAGA (SEQ ID NO:123) C (SEQ ID NO:124) ATAG (SEQ ID NO:125) hCV2259921 rs20541 A/G CTTTCGAAGTTTCAGTT TTCGAAGTTTCAGTTGAA GTTTTCCAGCTTGCATGTC GAACT (SEQ ID NO:126) CC (SEQ ID NO:127) (SEQ ID NO:128) hCV2346958 rs157578 C/G GGCAAGAATAGTGCCT GGCAAGAATAGTGCCTCG TGGCCCCAGCTGATCTCA CC (SEQ ID NO:129) (SEQ ID NO:130) (SEQ ID NO:131) hCV25603190 rs11568506 A/G TGGTGCTTTTAGAAGTG TGGTGCTTTTAGAAGTGA CTCCATCTTTGTCCATGCCCA rs41542912 ATACTTTT TACTTTC (SEQ ID NO:133) AATA (SEQ ID NO:134) (SEQ ID NO:132) hCV26479272 rs4594848 A/C GATGAGACAGCTTCTT TGAGACAGCTTCTTGTTTA GCCCCCTGTGAAATCACCATA GTTTAACTA ACTC (SEQ ID NO:136) TTA (SEQ ID NO:137) (SEQ ID NO:135) hCV28028637 rs4143832 G/T CAGAACAGGGGCATTA CAGAACAGGGGCATTACT AGCTTTTGCCAGGGTGTTTG CTTG (SEQ ID NO:138) TT (SEQ ID NO:139) (SEQ ID NO:140) hCV29134417 rs6884762 C/T GTCTTTGTAATTAACAA GTCTTTGTAATTAACAAGT CAAGCAAGATCTGTCAGTGGA GTATCTTAGGAG ATCTTAGGAA TACT (SEQ ID NO:143) (SEQ ID NO:141) (SEQ ID NO:142) hCV29575948 rs10069772 A/G CAGAGCAAACCTAAGA CAGAGCAAACCTAAGACA GCCAAACAAGCCAGTAAATAG CACA (SEQ ID NO:144) CG (SEQ ID NO:145) AATCAGT (SEQ ID NO:146) hCV31237680 rs12186803 A/G TGACACTTGGCATGTTT TGACACTTGGCATGTTTA CACAGTTGCTGAGCTGTCAAG ACTT (SEQ ID NO:147) CTC (SEQ ID NO:148) ATAT (SEQ ID NO:149) hCV50000065 rs1800925 C/T TCTGGAGGACTTCTAG TCTGGAGGACTTCTAGGA TTCCCTTCTGCAGAATGAGT GAAAAC AAAT (SEQ ID NO:151) (SEQ ID NO:152) (SEQ ID NO:150) hCV559494 rs334902 C/G CATCCCAGGGAAGATG CATCCCAGGGAAGATGAA TCTCCGCAGTCAGCTTGTCTT AAG (SEQ ID NO:153) C (SEQ ID NO:154) A (SEQ ID NO:155) hCV8932051 rs848 C/A AGCACTAAAGCAGTGG CAGCACTAAAGCAGTGGA TGTTCTGCCCCTCTCCTGAC ACC (SEQ ID NO:156) CA (SEQ ID NO:157) (SEQ ID NO:158) hDV70977429 rs17618604 C/T GCCTCCTGGTCACCTC GCCTCCTGGTCACCTT GCTGCCATATCCCATCACTGA (SEQ ID NO:159) (SEQ ID NO:160) T (SEQ ID NO:161) hDV70978086 rs17622208 A/G GAACAGCCATACTTTTA ACAGCCATACTTTTAGCC GTGAGTGTGTTTAGGACCACT GCCAT (SEQ ID NO:162) AC (SEQ ID NO:163) TTAGTC (SEQ ID NO:164)

TABLE 4 Linkage Disequilibrium (LD) SNPs Interrogated SNP Interrogated rs LD SNP LD SNP rs Power Threshold r² r² hCV16176374 rs2227282 hCV11740439 rs2023823 0.51 0.574147858 0.8788 hCV16176374 rs2227282 hCV11818497 rs4266392 0.51 0.574147858 0.8824 hCV16176374 rs2227282 hCV11818498 rs4425499 0.51 0.574147858 0.84 hCV16176374 rs2227282 hCV11818499 rs2897442 0.51 0.574147858 0.8824 hCV16176374 rs2227282 hCV11818501 rs11740584 0.51 0.574147858 0.84 hCV16176374 rs2227282 hCV11818506 rs17690965 0.51 0.574147858 0.8645 hCV16176374 rs2227282 hCV11818513 rs2227284 0.51 0.574147858 1 hCV16176374 rs2227282 hCV15751509 rs2299009 0.51 0.574147858 0.8824 hCV16176374 rs2227282 hCV15793498 rs2406539 0.51 0.574147858 0.9538 hCV16176374 rs2227282 hCV15955623 rs2237059 0.51 0.574147858 0.5957 hCV16176374 rs2227282 hCV16164369 rs2074529 0.51 0.574147858 0.8824 hCV16176374 rs2227282 hCV16177653 rs2285700 0.51 0.574147858 0.84 hCV16176374 rs2227282 hCV2558119 rs9784600 0.51 0.574147858 0.6046 hCV16176374 rs2227282 hCV2558124 rs10036532 0.51 0.574147858 0.8336 hCV16176374 rs2227282 hCV26478846 rs6864565 0.51 0.574147858 0.84 hCV16176374 rs2227282 hCV26478847 rs6864396 0.51 0.574147858 0.84 hCV16176374 rs2227282 hCV26478954 rs4426908 0.51 0.574147858 1 hCV16176374 rs2227282 hCV27472356 rs3213639 0.51 0.574147858 0.84 hCV16176374 rs2227282 hCV30117349 rs9784675 0.51 0.574147858 0.5818 hCV16176374 rs2227282 hCV30585769 rs10062446 0.51 0.574147858 0.84 hCV16176374 rs2227282 hCV31237596 rs3798129 0.51 0.574147858 0.5818 hCV16176374 rs2227282 hCV31237618 rs11242127 0.51 0.574147858 0.8824 hCV16176374 rs2227282 hCV31237619 rs4705965 0.51 0.574147858 0.8824 hCV16176374 rs2227282 hCV31237737 rs11242122 0.51 0.574147858 0.9576 hCV16176374 rs2227282 hCV31237741 rs11747814 0.51 0.574147858 0.9576 hCV16176374 rs2227282 hCV8932022 rs1468215 0.51 0.574147858 0.84 hCV2259921 rs20541 hCV8932046 rs847 0.51 0.893902117 0.9061 hCV2259921 rs20541 hCV8932051 rs848 0.51 0.893902117 0.955 hCV2259921 rs20541 hCV8932052 rs1295685 0.51 0.893902117 0.955 hCV2259921 rs20541 hCV8932053 rs1295686 0.51 0.893902117 1 hCV50000065 rs1800925 hCV11740472 rs2040704 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV11818538 rs2706348 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV11818554 rs12652920 0.51 0.783364112 0.8525 hCV50000065 rs1800925 hCV15756644 rs2301713 0.51 0.783364112 0.8519 hCV50000065 rs1800925 hCV15793510 rs2252775 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV15892310 rs2246176 0.51 0.783364112 0.8525 hCV50000065 rs1800925 hCV15892315 rs2522403 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV16098376 rs2106984 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV16154343 rs2897443 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV16164126 rs2074369 0.51 0.783364112 0.8519 hCV50000065 rs1800925 hCV16274080 rs2706347 0.51 0.783364112 0.8567 hCV50000065 rs1800925 hCV16274083 rs2244012 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV16274086 rs2706338 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV16274088 rs2706372 0.51 0.783364112 0.8479 hCV50000065 rs1800925 hCV16274090 rs2706370 0.51 0.783364112 0.8046 hCV50000065 rs1800925 hCV25471631 rs2522394 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV2549970 rs10463893 0.51 0.783364112 0.8525 hCV50000065 rs1800925 hCV2549979 rs12653750 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV2549980 rs2040703 0.51 0.783364112 0.8567 hCV50000065 rs1800925 hCV2549981 rs2240032 0.51 0.783364112 0.8474 hCV50000065 rs1800925 hCV2549985 rs2299015 0.51 0.783364112 0.8292 hCV50000065 rs1800925 hCV25812184 rs3798134 0.51 0.783364112 0.8525 hCV50000065 rs1800925 hCV27452177 rs3091307 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV27484753 rs3798135 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV29134412 rs7737470 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV29134413 rs7449456 0.51 0.783364112 0.8418 hCV50000065 rs1800925 hCV29134415 rs6596086 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV30081308 rs6871536 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV30171484 rs10520114 0.51 0.783364112 0.8573 hCV50000065 rs1800925 hCV31237845 rs6596087 0.51 0.783364112 0.8525 hCV50000065 rs1800925 hDV70911845 rs17166050 0.51 0.783364112 0.8573 hCV8932051 rs848 hCV2259921 rs20541 0.51 0.85292942 0.955 hCV8932051 rs848 hCV8932046 rs847 0.51 0.85292942 0.9537 hCV8932051 rs848 hCV8932052 rs1295685 0.51 0.85292942 1 hCV8932051 rs848 hCV8932053 rs1295686 0.51 0.85292942 0.955

TABLE 5 Association of IL13 SNPs with psoriasis Marker Genotypes Allelic Genotypic (Alleles & Position) Sample Set Status 11 12 22 MAF HWE OR (95% CI) P P rs1800925 1 case 338 112 11 0.145 0.577 (T132020708C) control 303 139 14 0.183 0.756 0.76 (0.59-0.97) 0.0320 0.0781 2 case 335 150 9 0.170 0.111 control 316 158 21 0.202 0.782 0.75 (0.60-0.94) 0.0162 0.0297 3 case 345 119 16 0.157 0.166 control 272 131 21 0.204 0.373 0.73 (0.57-0.93) 0.0050 0.0205 Combined 0.76 (0.67-0.88) 2.50E−04 0.0029 rs20541 1 case 326 131 6 0.154 0.106 (T132023863C) control 298 140 20 0.197 0.462 0.75 (0.59-0.95) 0.0198 0.0092 2 case 339 143 12 0.169 0.630 control 312 170 12 0.196 0.047 0.83 (0.66-1.05) 0.0650 0.0907 3 case 341 131 8 0.153 0.376 control 272 138 12 0.192 0.347 0.76 (0.60-0.97) 0.0166 0.0416 Combined 0.78 (0.68-0.90) 0.0013 0.0022 rs848 1 case 323 132 6 0.156 0.076 (T132024399G) control 294 145 19 0.200 0.884 0.74 (0.58-0.94) 0.0148 0.0113 2 case 338 144 12 0.170 0.527 control 308 171 13 0.200 0.067 0.82 (0.65-1.03) 0.0463 0.0784 3 case 334 138 8 0.160 0.175 control 269 141 13 0.197 0.358 0.78 (0.61-0.99) 0.0210 0.0503 Combined 0.78 (0.68-0.89) 0.0017 0.0027

TABLE 6 Three-marker IL13 haplotypes (the order of SNPs is rs1800925-rs20541-rs848) Sample Set 1 Sample Set 2 467 cases/460 controls 498 cases/498controls Global P = 0.093 Global P = 0.151 No. (Frequency) in No. (Frequency) in Haplotype Case Control P OR Case Control P OR CCG 723 (0.777)  677 (0.736) 0.0288 1.26 749 (0.758)  710 (0.717) 0.0177 1.24 TTT 74 (0.079) 108 (0.117) 0.0063 0.65 97 (0.098) 115 (0.116) 0.0653 0.83 TCG 61 (0.066)  61 (0.066) 0.9284 0.99 70 (0.071)  83 (0.084) 0.1503 0.83 CTT 69 (0.074)  72 (0.079) 0.6234 0.94 69 (0.07)   80 (0.081) 0.2072 0.85 Other  3 (0.003)  3 (0.003) NC NC  3 (0.003)  2 (0.002) NC NC Sample Set 3 481 cases/424controls Global P = 0.025 Combined No. (Frequency) in Global P_(comb) = 0.014 Haplotype Case Control P OR P_(comb) OR_(MH) CCG 739 (0.77)  606 (0.714)  0.0037 1.34 1.88E−04 1.27 TTT 84 (0.087) 96 (0.113) 0.0213 0.75 7.05E−04 0.74 TCG 66 (0.069) 72 (0.085) 0.0999 0.80 0.201 0.86 CTT 62 (0.065) 66 (0.078) 0.1545 0.82 0.251 0.87 Other 9 (0.01) 8 (0.01) NC NC NC NC

TABLE 7 HLA-cytokine pathway psoriasis relative risk estimates HLA-C IL12B/IL23R/IL13^(†) P (MLG) P (Psoriasis|MLG) RR RR (95% CI) SRR *0602 carriers Very High Risk 0.0296 0.1149 3.8286 (2.359-7.277) 11.3411 *0602 carriers High Risk 0.0733 0.0789 2.6294 (1.874-3.839) 7.7890 *0602 carriers Moderate Risk 0.0534 0.0539 1.7971 (1.150-2.922) 5.3235 *0602 carriers Low Risk 0.0121 0.0368 1.2266 (0.377-4.784) 3.6334 noncarriers Very High Risk 0.1417 0.0336 1.1195 (0.827-1.511) 3.3162 noncarriers High Risk 0.3608 0.0224 0.7474 (0.617-0.897) 2.2139 noncarriers Moderate Risk 0.2677 0.0150 0.5011 (0.378-0.650) 1.4843 noncarriers Low Risk 0.0615 0.0101 0.3376 (0.140-0.649) 1.0000 Cytokine Pathway IL12B Diplotypes IL23R Diplotypes IL13 Genotypes HLA-C Risk Category (rs6887695-rs3212227) (rs7530511-rs11209026) (rs1800925) *0602 carriers Very High Risk AG/AG CG/CG CC *0602 carriers High Risk AG/AG CG/CG Other *0602 carriers High Risk AG/AG Other CC *0602 carriers High Risk Other CG/CG CC noncarriers Moderate Risk AG/AG Other Other noncarriers Moderate Risk Other CG/CG Other noncarriers Moderate Risk Other Other CC noncarriers Low Risk Other Other Other ^(†Definition of IL12B/IL23R/IL13 Risk Groups)

TABLE 8 Table 1 of Example Two. Association analysis of two independent SNPs in the three sample sets combined SNP ID Gene Allele/Genotype Cases N (%) Controls N (%) OR (95% CI) P rs11568506 SLC22A4 A 50 (0.017) 69 (0.025) 0.68 (0.47-0.99) 0.043 G 2820 (0.983) 2677 (0.975) 1.00 (reference) AA 2 (0.0014) 0 (0) NC* 0.022 AG 46 (0.032) 69 (0.050) 0.63 (0.43-0.92) GG 1387 (0.0967) 1304 (0.950) 1.00 (reference) rs1800925 IL13 T 453 (0.158) 540 (0.196) 0.77 (0.67-0.88) 0.00015 C 2417 (0.842) 2210 (0.804) 1.00 (reference) TT 36 (0.025) 56 (0.041) 0.56 (0.37-0.86) 0.00081 TC 381 (0.266) 428 (0.311) 0.78 (0.66-0.92) CC 1018 (0.709) 891 (0.648) 1.00 (reference) *not calculated See supplementary Table 2 for other significant markers

TABLE 9 Table 2. Conditional association tests for 5q31 markers: P-values for marker 1 conditional on marker 2 Marker 1 rs11568506 rs4143832 rs2897443 rs6884762 rs1800925 rs20541 Marker 2 rs11568506 0.0138 0.0012 0.0023 0.0002 0.0012 rs4143832 0.0022 0.2503 0.0067 0.0046 0.0591 rs2897443 0.0071 0.9922 0.0736 0.0423 0.1103 rs6884762 0.0114 0.0915 0.0740 0.0118 0.0129 rs1800925 0.0105 0.2493 0.2168 0.0648 0.1538 rs20541 0.0195 0.8541 0.2509 0.0432 0.1123 rs848 0.0197 0.6773 0.3075 0.0361 0.0676 0.7771 rs2243211 0.0145 0.5871 0.2605 0.0553 0.0330 0.0578 rs762534 0.0202 0.3471 0.2286 0.0302 0.0378 0.0822 rs2227282 0.1132 0.6841 0.1164 0.0588 0.0563 0.1368 rs12186803 0.1977 0.4103 0.0326 0.0005 0.0151 0.0153 rs10069772 0.0281 0.1877 0.0327 0.0691 0.0054 0.0642 Marker 1 rs848 rs2243211 rs762534 rs2227282 rs12186803 rs10069772 Marker 2 rs11568506 0.0012 0.0023 0.0094 0.0093 0.3754 0.0390 rs4143832 0.0353 0.0791 0.1072 0.0531 0.3720 0.0873 rs2897443 0.1386 0.1807 0.4214 0.0552 0.2559 0.1870 rs6884762 0.0099 0.0648 0.0608 0.0148 0.0239 0.2777 rs1800925 0.1006 0.1136 0.3292 0.0888 0.4419 0.1153 rs20541 0.8374 0.1031 0.4035 0.1524 0.3309 0.5399 rs848 0.2661 0.3943 0.2287 0.5391 0.5746 rs2243211 0.1443 0.2897 0.0048 0.0992 0.1132 rs762534 0.0742 0.0999 0.0034 0.0879 0.0365 rs2227282 0.1943 0.0238 0.0276 0.8115 0.8433 rs12186803 0.0342 0.0040 0.0079 0.0557 0.0116 rs10069772 0.0556 0.0372 0.0167 0.0878 0.0289 Note: P < 0.05 in boldface

TABLE 10 Table 3. Haplotype association tests Haplotype^(a) (rs11568506/ Case Count Control Count Sample set rs1800925) (Freq.) (Freq.) P_(haplotype) Global P OR SS1 GC 771 (0.844) 713 (0.787) 0.0019 0.0028 1.46 GT 131 (0.143) 167 (0.184) 0.018 0.74 AC 12 (0.013) 26 (0.029) 0.019 0.45 SS2 GC 796 (0.807) 765 (0.781) 0.13 0.11 1.18 GT 168 (0.170) 199 (0.203) 0.052 0.81 AC 22 (0.022) 15 (0.015) 0.36 1.48 SS3 GC 799 (0.830) 651 (0.764) 9.11E−04 0.0028 1.51 GT 147 (0.153) 175 (0.205) 6.87E−03 0.70 AC 13 (0.014) 26 (0.031) 0.031 0.45 All^(b) GC 2366 (0.827) 2128 (0.777) 5.67E−06 8.93E−05 1.37 GT 446 (0.156) 542 (0.198) 6.01E−05 0.75 AC 47 (0.017) 68 (0.025) 0.053 0.66 ^(a)Frequency of AT haplotype is <0.001 in both cases or controls. ^(b)Because haplotype counts were estimated, these counts may vary slightly from the sum of the three individudal sample sets.

TABLE 11 Table 4. Association of putative CD SNPs with psoriasis Psoriasis (SS1 + SS2 + SS3) CD (WTCCC)^(a) Case Case Control allele Control allele allele fre- allele fre- fre- Marker Chr Position (bp) Gene quency frequency P_(allelic) OR (95% CI) quency quency P_(allelic) OR (95% CI) rs6596075 5 131,770,127 0.162 0.164 0.82 0.98 (0.85-1.14) 0.127 0.166 5.40E−07 0.73 (0.65-0.83) rs2285673 5 131,783,868 LOC441108 0.226 0.235 0.43 0.95 (0.84-1.08) 0.212 0.252 1.50E−05 0.80 (0.73-0.89) rs4540166 5 131,807,756 LOC441108 0.205 0.212 0.52 0.96 (0.84-1.09) 0.189 0.228 8.98E−06 0.79 (0.71-0.88) rs10077785 5 131,829,057 0.209 0.218 0.45 0.95 (0.84-1.09) 0.192 0.234 1.81E−06 0.78 (0.70-0.86) rs2522057 5 131,829,846 0.444 0.414  0.022 1.13 (1.02-1.26) 0.478 0.422 1.01E−07 1.29 (1.20-1.33) ^(a)Data are from WTCCC (3).

TABLE 12 Association results in sample set 1. Case^(b) Control^(b) RS Number Gene Position and alleles^(a) 11 12 22 HWE^(c) 11 12 22 HWE^(c) P_(allelic) P_(genotypic) rs12656759 C131395509T 98 237 126 0.51 99 240 120 0.35 0.75 0.92 rs2073506 T131422637C 4 63 394 0.33 2 71 386 0.76 0.71 0.55 rs31481 IL3 A131425101G 13 107 339 0.19 12 120 325 0.86 0.45 0.59 rs11575022 C131429914A 2 43 416 0.33 7 42 410 0.0006 0.35 0.23 rs2069626 CSF2 G131438678A 0 15 446 1 0 9 450 1 0.22 rs743564 CSF2 C131438778T 76 228 155 0.63 72 222 165 0.92 0.51 0.78 rs25882 CSF2 C131439359T 18 142 298 0.88 19 150 290 1 0.57 0.84 rs25887 C131443960A 83 232 146 0.64 87 223 149 0.85 0.96 0.86 rs25888 A131444345G 0 25 436 1 0 21 438 1 0.56 rs31467 C131464737T 106 233 122 0.85 116 227 116 0.85 0.46 0.71 rs39897 C131464795T 96 235 130 0.64 93 239 127 0.35 0.96 0.95 rs154735 A131465431G 2 56 402 1 1 56 402 1 0.86 0.86 rs247008 A131475003G 42 198 221 0.91 41 213 205 0.20 0.47 0.56 rs156055 A131528076G 4 65 390 0.51 3 64 391 0.74 0.80 0.93 rs156033 T131555220C 12 126 323 1 11 131 317 0.74 0.82 0.91 rs2278398 P4HA2 G131558340A 100 244 117 0.23 97 229 132 0.93 0.40 0.49 rs156030 P4HA2 A131558816G 0 49 411 0.63 2 33 424 0.16 0.19 rs152050 T131564725C 52 215 193 0.54 54 217 186 0.48 0.69 0.92 rs10520127 P4HA2 T131567154C 2 64 394 1 3 68 387 1 0.58 0.83 rs17618604 P4HA2 T131570083C 9 84 368 0.15 2 112 345 0.02 0.30 0.01 rs283763 P4HA2 T131581144A 14 159 288 0.19 16 162 281 0.26 0.65 0.89 rs7733814 P4HA2 T131584208C 2 59 400 1 1 55 401 1 0.60 0.81 rs157578 P4HA2 C131589221G 3 102 356 0.18 12 108 339 0.34 0.09 0.04 rs10036208 T131599222C 48 206 207 0.83 62 186 211 0.05 0.64 0.24 rs11749843 A131600397G 0 8 452 1 0 5 454 1 0.41 rs2405271 C131601292G 11 151 299 0.13 19 138 302 0.55 0.82 0.26 rs334902 C131614458G 78 244 139 0.11 101 207 151 0.06 0.61 0.04 rs4594848 A131614497C 94 247 120 0.13 107 208 143 0.07 0.63 0.05 rs7727038 C131618156T 86 226 149 1 98 214 147 0.22 0.52 0.57 rs162881 PDLIM4 G131636693T 4 73 383 0.77 5 85 369 1 0.26 0.54 rs162896 T131640775C 0 33 427 1 0 20 439 1 0.07 rs7737937 A131642880G 10 125 325 0.73 8 118 333 0.71 0.49 0.77 rs162899 G131643492A 26 175 260 0.71 25 178 254 0.45 0.87 0.95 rs272895 G131644273A 6 114 341 0.43 6 101 352 0.83 0.39 0.63 rs381870 T131650200A 11 146 304 0.21 18 142 299 0.77 0.52 0.41 rs162892 A131651149G 47 210 203 0.53 48 194 216 0.67 0.53 0.59 rs162889 T131652285C 18 191 252 0.02 22 177 260 0.31 0.78 0.59 rs2662314 T131653068C 7 115 338 0.56 7 107 344 0.84 0.61 0.85 rs157572 C131654011G 39 204 218 0.44 38 187 234 0.91 0.36 0.52 rs3792876 SLC22A4 T131665208C 4 62 394 0.32 3 60 396 0.72 0.73 0.92 rs3792884 SLC22A4 G131679160A 5 90 361 1 3 90 361 0.45 0.79 0.78 rs11739484 SLC22A4 G131684659A 0 25 436 1 0 23 436 1 0.78 rs273912 SLC22A4 G131689248T 37 186 238 0.91 50 179 230 0.10 0.30 0.33 rs272879 SLC22A4 C131698445G 61 211 189 0.84 69 209 181 0.49 0.45 0.72 rs11568506 SLC22A4 A131699359G 0 12 449 1 0 27 430 1 0.01 rs17622208 SLC22A5 A131744949G 97 252 112 0.05 114 208 137 0.05 0.71 0.02 rs7731390 SLC22A5 G131749648C 0 47 413 0.62 1 38 420 0.59 0.45 rs17689550 SLC22A5 T131750964C 5 86 370 1 6 96 356 1 0.35 0.64 rs35306350 SLC22A5 T131751187C 81 253 125 0.02 102 213 143 0.19 0.89 0.03 rs274549 SLC22A5 A131757017C 13 140 307 0.64 7 135 317 0.09 0.30 0.36 rs11739135 C131761296G 79 214 167 0.50 79 216 161 0.70 0.80 0.95 rs6596075 G131770127C 13 140 307 0.64 7 135 317 0.09 0.30 0.36 rs4705943 LOC441108 G131782903A 6 87 368 0.63 7 80 372 0.30 0.73 0.83 rs4705944 LOC441108 A131783163T 1 31 429 0.45 3 26 430 0.01 0.91 0.50 rs2285673 LOC441108 T131783868C 25 183 252 0.32 25 178 255 0.45 0.83 0.96 rs1004234 LOC441108 A131785000G 2 56 401 1 0 54 405 0.39 0.56 rs11958162 LOC441108 A131799815T 8 105 348 1 3 107 349 0.13 0.60 0.31 rs4540166 LOC441108 T131807756C 19 164 277 0.50 22 158 279 1 0.96 0.85 rs7713818 LOC441108 T131816650C 45 201 213 0.91 46 216 197 0.25 0.40 0.56 rs10477741 LOC441108 G131823209T 7 94 358 0.82 5 95 359 0.82 0.83 0.85 rs6894249 LOC441108 G131825446A 64 231 166 0.28 59 230 169 0.20 0.68 0.89 rs2522051 LOC441108 C131825477T 81 241 136 0.16 108 208 143 0.06 0.35 0.04 rs10077785 T131829057C 20 164 277 0.59 19 163 277 0.50 0.90 0.99 rs2522057 G131829846C 79 237 145 0.34 79 222 157 1 0.56 0.62 rs2522064 A131834387G 10 147 304 0.12 20 148 290 0.89 0.19 0.16 rs17715481 G131843283A 1 67 393 0.50 0 77 382 0.06 0.47 rs10072700 C131844802A 37 174 249 0.41 29 174 256 1 0.41 0.59 rs2070721 IRF1 C131853741T 98 219 144 0.40 90 227 141 1 0.80 0.78 rs2548997 G131863294A 58 195 207 0.25 47 208 203 0.60 0.70 0.45 rs4143832 T131890876G 12 102 345 0.17 17 126 316 0.33 0.03 0.10 rs4705959 C131893690T 26 179 253 0.54 32 159 268 0.21 0.65 0.33 rs2706399 A131895601G 104 232 124 0.85 103 242 107 0.16 0.46 0.50 rs739719 A131900764C 6 72 383 0.25 2 71 385 0.76 0.48 0.36 rs2069822 IL5 C131906781T 0 19 441 1 1 15 442 0.14 0.75 rs2069812 A131907815G 40 216 205 0.13 43 194 220 1 0.50 0.41 rs2897443 RAD50 T131957493G 11 111 338 0.57 14 137 308 0.88 0.04 0.11 rs6884762 RAD50 T131966629C 0 15 446 1 0 34 424 1 0.01 rs17772583 RAD50 G131981409A 27 180 253 0.54 30 163 266 0.45 0.57 0.52 rs2237060 RAD50 G131998784T 93 234 134 0.64 87 222 150 0.78 0.30 0.49 rs2158177 RAD50 G132011957A 8 112 339 0.85 12 133 314 0.74 0.06 0.17 rs1800925 T132020708C 11 112 338 0.58 14 139 303 0.76 0.03 0.08 rs20541 IL13 A132023863G 6 131 326 0.11 20 140 298 0.46 0.02 0.01 rs848 IL13 A132024399C 6 132 323 0.08 19 145 294 0.88 0.01 0.01 rs1295683 A132026775G 3 78 380 1 6 70 383 0.24 0.89 0.49 rs2243297 A132027070T 0 26 435 1 2 34 423 0.18 0.12 rs2243211 A132029321C 0 54 406 0.39 5 72 379 0.39 0.01 rs762534 A132032655C 0 47 414 0.62 3 69 387 1 0.01 rs2243248 G132036543T 3 50 408 0.23 1 71 387 0.34 0.11 0.08 rs2227282 IL4 C132041078G 18 173 270 0.15 45 162 251 0.02 0.02 0.00 rs2243263 IL4 C132041198G 3 84 373 0.60 9 90 360 0.26 0.17 0.17 rs12186803 KIF3A A132067968G 4 103 353 0.38 15 110 333 0.15 0.04 0.02 rs10069772 KIF3A A132068587G 4 90 362 0.81 11 105 343 0.43 0.04 0.08 rs17691077 KIF3A C132071250A 9 102 349 0.68 7 97 355 0.83 0.54 0.81 ^(a)Positions according to genomic contig NT_034772.5 (Entrez Nucleotide). The minor allele is listed first, followed by the position in National Center for Biotechnology Information Genome Build 36.2 and then the major allele. ^(b)Counts of genotype 11, 12 and 22. ^(c)P-value from Hardy-Weinberg Equilibrium test ^(d)The data for these three SNPs was reported previously (21).

TABLE 13 Minor allele frequencies and allele-based association of 5q31 SNPs with psoriasis Position and ^(b)Case ^(b)Control RS Number Public alleles^(a) Study 11 12 22 Sum MAF HWE 11 12 rs11568506 SLC22A4 A131699359G SS1 0 12 449 461 0.013 1 0 27 SS2 2 18 473 493 0.022 0.02 0 16 SS3 0 16 465 481 0.017 1 0 26 All 2 46 1387 1435 0.017 0 69 rs4143832 LOC441108 T131890876G SS1 12 102 345 459 0.137 0.17 17 126 SS2 11 151 331 493 0.175 0.22 21 153 SS3 15 129 337 481 0.165 0.51 15 117 All 38 382 1013 1433 0.160 53 396 rs2897443 RAD50 T131957493G SS1 11 111 338 460 0.145 0.57 14 137 SS2 9 161 323 493 0.182 0.03 22 154 SS3 14 124 344 482 0.158 0.49 22 130 All 34 396 1005 1435 0.162 58 421 rs6884762 RAD50 T131966629C SS1 0 15 446 461 0.016 1 0 34 SS2 0 30 463 493 0.030 1 0 30 SS3 0 16 466 482 0.017 1 0 31 All 0 61 1375 1436 0.021 0 95 rs1800925 IL13 T132020708C SS1 11 112 338 461 0.145 0.58 14 139 SS2 9 150 335 494 0.170 0.11 21 158 SS3 16 119 345 480 0.157 0.17 21 131 All 36 381 1018 1435 0.158 56 428 rs20541 IL13 A132023863G SS1 6 131 326 463 0.154 0.11 20 140 SS2 12 143 339 494 0.169 0.63 12 170 SS3 8 131 341 480 0.153 0.38 12 138 All 26 405 1006 1437 0.159 44 448 rs848 IL13 A132024399C SS1 6 132 323 461 0.156 0.076 19 145 SS2 12 144 338 494 0.170 0.53 13 171 SS3 8 138 334 480 0.160 0.18 13 141 All 26 414 995 1435 0.162 45 457 rs2243211 A132029321C SS1 0 54 406 460 0.059 0.39 5 72 SS2 0 51 441 492 0.052 0.63 3 67 SS3 1 64 416 481 0.069 0.72 2 61 All 1 169 1263 1433 0.060 10 200 rs762534 A132032655C SS1 0 47 414 461 0.051 0.62 3 69 SS2 1 54 437 492 0.057 1 4 67 SS3 1 48 433 482 0.052 1 1 48 All 2 149 1284 1435 0.053 8 184 rs2227282 IL4 C132041078G SS1 18 173 270 461 0.227 0.15 45 162 SS2 27 188 279 494 0.245 0.63 34 193 SS3 31 143 306 480 0.214 0.01 31 172 All 76 504 855 1435 0.229 110 527 rs12186803 KIF3A A132067968G SS1 4 103 353 460 0.121 0.38 15 110 SS2 10 111 373 494 0.133 0.56 7 111 SS3 7 86 389 482 0.104 0.33 12 94 All 21 300 1115 1436 0.119 34 315 rs10069772 KIF3A A132068587G SS1 4 90 362 456 0.107 0.81 11 105 SS2 10 99 381 490 0.121 0.29 11 120 SS3 5 101 372 478 0.116 0.66 6 101 All 19 290 1115 1424 0.115 28 326 Position and ^(b)Control RS Number Public alleles^(a) Study 22 Sum MAF HWE P^(c) OR (95% CI) rs11568506 SLC22A4 A131699359G SS1 430 457 0.030 1 0.014 0.43 (0.21-0.86) SS2 474 490 0.016 1 0.34 1.37 (0.71-2.63) SS3 400 426 0.031 1 0.050 0.53 (0.28-1.00) All 1304 1373 0.025 0.043 0.68 (0.47-0.99) rs4143832 LOC441108 T131890876G SS1 36 459 0.174 0.33 0.029 0.75 (0.58-0.97) SS2 318 492 0.198 0.67 0.20 0.86 (0.68-1.08) SS3 294 426 0.173 0.40 0.68 0.94 (0.74-1.21) All 928 1377 0.182 0.026 0.85 (0.74-0.98) rs2897443 RAD50 T131957493G SS1 308 459 0.180 0.88 0.041 0.77 (0.60-0.98) SS2 316 492 0.201 0.57 0.27 0.88 (0.70-1.10) SS3 273 425 0.205 0.23 0.0092 0.72 (0.57-0.92) All 897 1376 0.195 0.0010 0.78 (0.69-0.91) rs6884762 RAD50 T131966629C SS1 424 458 0.037 1 0.0055 0.42 (0.23-0.79) SS2 462 492 0.030 1 0.96 0.99 (0.59-1.66) SS3 394 425 0.036 1 0.0078 0.44 (0.24-0.82) All 1280 1375 0.035 0.0025 0.61 (0.44-0.84) rs1800925 IL13 T132020708C SS1 303 456 0.183 0.76 0.029 0.76 (0.59-0.97) SS2 316 495 0.202 0.78 0.068 0.81 (0.64-1.02) SS3 272 424 0.204 0.37 0.010 0.73 (0.57-0.93) All 891 1375 0.196 0.00015 0.77 (0.67-0.88) rs20541 IL13 A132023863G SS1 298 458 0.197 0.46 0.018 0.75 (0.59-0.95) SS2 312 494 0.196 0.047 0.12 0.83 (0.66-1.05) SS3 272 422 0.192 0.35 0.029 0.76 (0.60-0.97) All 882 1374 0.195 0.00043 0.78 (0.68-0.90) rs848 IL13 A132024399C SS1 294 458 0.200 0.88 0.015 0.74 (0.58-0.94) SS2 308 492 0.200 0.067 0.085 0.82 (0.65-1.03) SS3 269 423 0.197 0.36 0.040 0.78 (0.61-0.99) All 871 1373 0.199 0.00035 0.78 (0.68-0.89) rs2243211 A132029321C SS1 379 456 0.090 0.39 0.011 0.63 (0.44-0.90) SS2 420 490 0.074 0.74 0.039 0.67 (0.46-0.98) SS3 362 425 0.076 1 0.52 0.88 (0.62-1.26) All 1161 1371 0.080 0.0024 0.72 (0.58-0.89) rs762534 A132032655C SS1 387 459 0.082 1 0.0081 0.60 (0.41-0.87) SS2 420 491 0.076 0.51 0.084 0.72 (0.51-1.04) SS3 376 425 0.059 1 0.52 0.87 (0.58-1.31) All 1183 1375 0.073 0.0031 0.72 (0.58-0.89) rs2227282 IL4 C132041078G SS1 251 458 0.275 0.02 0.017 0.77 (0.62-0.95) SS2 265 492 0.265 1 0.30 0.89 (0.73-1.10) SS3 223 426 0.275 0.90 0.0024 0.71 (0.57-0.88) All 739 1376 0.271 0.00022 0.79 (0.70-0.89) rs12186803 KIF3A A132067968G SS1 333 458 0.153 0.15 0.045 0.76 (0.58-0.99) SS2 374 492 0.127 0.84 0.71 1.05 (0.80-1.36) SS3 320 426 0.138 0.15 0.023 0.71 (0.54-0.95) All 1027 1376 0.139 0.027 0.83 (0.71-0.98) rs10069772 KIF3A A132068587G SS1 343 459 0.138 0.43 0.044 0.74 (0.56-0.99) SS2 359 490 0.145 0.72 0.13 0.81 (0.62-1.05) SS3 317 424 0.133 0.67 0.27 0.85 (0.64-1.13) All 1019 1373 0.139 0.0074 0.80 (0.68-0.94) ^(a)Positions according to genomic contig NT_034772.5 (Entrez Nucleotide). The minor allele is listed first, followed by the position in National Center for Biotechnology Information Genome Build 36.2 and then the major allele. ^(b)Counts for genotypes 11, 12 and 22; MAF: minor allele frequency: HWE: Hardy-Weinberg P-value ^(c)P values and ORs are calculated by chi-square method for SS1, SS2 and SS3 and by Mantel-Haenszel method for the three sample sets combined (All); P-values for the three IL13 markers in the combined sample sets are slightly different from our previous report which used Fisher's exact test (21).

TABLE 14 LD measures of the twelve significant markers in controls of sample set 1. D′ rs11568506 rs4143832 rs2897443 rs6884762 rs1800925 rs20541 r² rs11568506 0.394 0.467 0.04 0.953 0.996 rs4143832 0.001 0.721 0.918 0.747 0.296 rs2897443 0.001 0.501 1 0.908 0.525 rs6884762 0.001 0.154 0.177 1 0.56 rs1800925 0.006 0.517 0.801 0.174 0.552 rs20541 0.007 0.075 0.245 0.05 0.278 rs848 0.008 0.076 0.241 0.048 0.273 0.979 rs2243211 0.003 0.067 0.177 0.121 0.263 0.318 rs762534 0.003 0.091 0.169 0.133 0.212 0.245 rs2227282 0.01 0.067 0.066 0.031 0.059 0.12 rs12186803 0.027 0.049 0.044 0.007 0.035 0.057 rs10069772 0.005 0.007 0.011 0.061 0.016 0.046 D′ rs848 rs2243211 rs762534 rs2227282 rs12186803 rs10069772 r² rs11568506 0.997 1 1 0.349 0.408 0.993 rs4143832 0.3 0.378 0.465 0.349 0.24 0.097 rs2897443 0.524 0.628 0.646 0.34 0.231 0.123 rs6884762 0.556 0.549 0.555 0.557 0.987 0.505 rs1800925 0.55 0.778 0.735 0.316 0.21 0.148 rs20541 0.993 0.895 0.825 0.431 0.279 0.265 rs848 0.913 0.826 0.441 0.3 0.271 rs2243211 0.328 0.805 0.175 0.855 0.344 rs762534 0.244 0.588 0.26 1 0.415 rs2227282 0.128 0.008 0.016 0.956 0.849 rs12186803 0.065 0.013 0.016 0.436 0.999 rs10069772 0.047 0.074 0.096 0.305 0.029

TABLE 15 Chromosome 5q31 markers in high LD with rs1800925 r² with marker Position (bp) Gene Symbol SNPType 1800925 rs12652920 131,913,139 IL5 putative TFBS^(a) 0.895 rs2706338 131,923,748 RAD50 Intron 0.897 rs2244012 131,929,124 LOC441108 Intron 0.897 rs2299015 131,929,396 RAD50 Intron 0.877 rs2706347 131,933,016 RAD50 putative TFBS^(a) 0.897 rs2706348 131,933,709 RAD50 Intron 0.897 rs17166050 131,943,112 RAD50 putative TFBS^(a) 0.897 rs2522403 131,943,216 RAD50 Intron 0.897 rs2246176 131,945,249 RAD50 Intron 0.895 rs2252775 131,946,343 RAD50 Intron 0.897 rs10463893 131,955,938 RAD50 Intron 0.895 rs2897443 131,957,493 RAD50 putative TFBS^(a) 0.897 rs2706370 131,960,915 RAD50 Intron 0.829 rs2706372 131,963,376 RAD50 Intron 0.89 rs12187537 131,967,803 RAD50 Intron 0.817 rs2522394 131,972,028 RAD50 Intron 0.897 rs10520114 131,976,790 RAD50 Intron 0.897 rs2301713 131,979,895 RAD50 Intron 0.892 rs6596086 131,980,121 RAD50 Intron 0.897 rs2106984 131,980,965 RAD50 Intron 0.897 rs7449456 131,981,326 RAD50 Intron 0.89 rs3798135 131,993,008 RAD50 Intron 0.897 rs3798134 131,993,078 RAD50 Intron 0.894 rs6596087 131,996,508 RAD50 Intron 0.881 rs6871536 131,997,773 RAD50 Intron 0.897 rs12653750 131,999,801 RAD50 Intron 0.897 rs2040703 132,000,157 RAD50 Intron 0.897 rs2040704 132,001,076 RAD50 putative TFBS^(a) 0.897 rs2074369 132,001,562 RAD50 Intron 0.892 rs7737470 132,001,962 RAD50 Intron 0.897 rs2240032 132,005,026 RAD50 Intron 0.892 rs3091307 132,017,035 Intron 0.897 putative TFBS* (transcription factor binding site)

TABLE 16 Pairwise genotype counts for cases and controls. rs11568506 rs1800925 Cases N Controls N GG CC 978 831 GG CT 370 414 AG CC 38 52 GG TT 35 56 AG CT 7 16 AA CC 2 AG TT 1 

1. A method for determining whether a human has an altered risk for psoriasis or Crohn's Diesease, comprising testing nucleic acid from said human for the presence or absence of a single nucleotide polymorphism (SNP) as represented by position 101 of SEQ ID NOS: 5-9 and 19-110 or their complements, wherein the presence of the SNP indicates said human has an altered risk for psoriasis or Crohn's Diesease.
 2. The method of claim 1 in which the altered risk is an increased risk.
 3. The method of claim 1 in which the altered risk is a decreased risk.
 4. The method of claim 1, wherein the SNP is selected from the group consisting of rs1800925, rs20541, and rs848.
 5. The method of claim 1 in which detection is carried out by a process selected from the group consisting of: allele-specific probe hybridization, allele-specific primer extension, allele-specific amplification, sequencing, 5′ nuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, and single-stranded conformation polymorphism.
 6. The method of claim 1, wherein said nucleic acid is a nucleic acid extract from a biological sample from said human.
 7. The method of claim 1, wherein said biological sample is blood, saliva, or buccal cells.
 8. The method of claim 1, further comprising preparing said nucleic acid extract from said biological sample prior to said testing step.
 9. The method of claim 8, further comprising obtaining said biological sample from said human prior to said preparing step.
 10. The method of claim 1, wherein said testing step comprises nucleic acid amplification.
 11. The method of claim 10, wherein said nucleic acid amplification is carried out by polymerase chain reaction.
 12. The method of claim 1, further comprising correlating the presence or absence of said SNP with the altered risk for psoriasis or Crohn's Disease.
 13. The method of claim 1, wherein said correlating step is performed by computer software.
 14. A method for identifying an agent useful in therapeutically or prophylactically treating psoriasis, the method comprising contacting a polypeptide comprising an amino acid sequence selected from the group consisting of SEQ ID NOS: 3-4 with a candidate agent under conditions suitable to allow formation of a binding complex between the polypeptide and the candidate agent, and detecting the formation of the binding complex, wherein the presence of the complex identifies said agent.
 15. The method of claim 1, wherein the SNP comprises a SNP haplotype selected from the group consisting of: the risk haplotype of rs1800925(C), rs20541(C), and rs848(G); and the protective haplotype of rs 1800925(T), rs20541(T), and rs848(T).
 16. The method of claim 2, wherein the SNP comprises the risk haplotype of rs1800925(C), rs20541(C), and rs848(G).
 17. The method of claim 3, wherein the SNP comprises the protective haplotype of rs1800925(T), rs20541(T), and rs848(T).
 18. A method for determining a human's risk of developing psoriasis, the method comprising detecting a SNP haplotype selected from the group consisting of the risk haplotype of rs1800925(C), rs20541(C), and rs848(G) and the protective haplotype of rs1800925(T), rs20541(T), and rs848(T) in said human's nucleic acids, wherein the presence of the SNP haplotype is indicative of an altered risk of developing psoriasis in said human.
 19. A method for determining whether a human will likely benefit from a drug treatment targeting IL13, the method comprising testing nucleic acid from said human for the presence or absence of a SNP as represented by position 101 of SEQ ID NOS: 5-9 and 19-110 or their complement, wherein the presence or absence of the SNP indicates that said human is likely to benefit from the drug treatment.
 20. The method of claim 19, wherein the human is selected for inclusion in a clinical trial of the drug treatment.
 21. A method of treating a human who will likely benefit from a drug treatment targeting IL13, the method comprising testing nucleic acid from said human for the presence or absence of a SNP as represented by position 101 of SEQ ID NOS: 5-9 and 19-110 or their complement, wherein the presence or absence of the SNP indicates that said human is likely to benefit from the drug treatment, and administering said drug treatment to said human.
 22. The method of any one of claims 4, 15, or 18, wherein the method further comprises detecting at least one variant selected from the group consisting of HLA-C, SNPs in IL12B, and SNPs in IL23R.
 23. The method of claim 22, wherein the HLA-C variant is HLA-C*0602.
 24. The method of claim 22, wherein the SNPs in IL12 and IL23R are selected from the group consisting of: SNPs rs321227, rs3212220, rs7709212, and rs6887695; the psoriasis risk haplotype rs3212227(A)/rs6887695(G); the psoriasis protective haplotype rs3212227(C)/rs6887695(C); the psoriasis risk haplotype rs 11209026(G)/rs7530511(C); a SNP diplotype which is a combination of two copies of the risk haplotype, rs3212227(A)/rs6887695(G); a SNP diplotype which is a combination of two copies of the protective haplotype, rs3212227(C)/rs6887695(C); a SNP diplotype which is a combination of two copies of the risk haplotype rs11209026(G)/rs 7530511(C); and a two-locus diplotype which is a combination of rs3212227(A)/rs6887695(G)/rs11209026(G)/rs7530511(C).
 25. A kit for carrying out the method of claim 1, wherein the kit comprises at least one polynucleotide detection reagent, and wherein the polynucleotide detection reagent selectively hybridizes to said nucleic acid in the presence of said polymorphism and does not hybridize to said nucleic acid in the absence of said polymorphism. 